Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding

Collagen COL22A1 maintains vascular stability and mutations in COL22A1 are potentially associated with intracranial aneurysms

Collagen XXII (COL22A1) is a quantitatively minor collagen, which belongs to the family of fibril-associated collagens with interrupted triple helices. Its biological function has been poorly understood. Here, we used a genome-editing approach to generate a loss-of-function mutant in zebrafish col22a1. Homozygous mutant adults exhibit increased incidence of intracranial hemorrhages, which become more prominent with age and after cardiovascular stress. Homozygous col22a1 mutant embryos show higher sensitivity to cardiovascular stress and increased vascular permeability, resulting in a greater percentage of embryos with intracranial hemorrhages. Mutant embryos also exhibit dilations and irregular structure of cranial vessels. To test whether COL22A1 is associated with vascular disease in humans, we analyzed data from a previous study that performed whole-exome sequencing of 45 individuals from seven families with intracranial aneurysms. The rs142175725 single-nucleotide polymorphism was identified, which segregated with the phenotype in all four affected individuals in one of the families, and affects a highly conserved E736 residue in COL22A1 protein, resulting in E736D substitution. Overexpression of human wild-type COL22A1, but not the E736D variant, partially rescued the col22a1 loss-of-function mutant phenotype in zebrafish embryos. Our data further suggest that the E736D mutation interferes with COL22A1 protein secretion, potentially leading to endoplasmic reticulum stress. Altogether, these results argue that COL22A1 is required to maintain vascular integrity. These data further suggest that mutations in COL22A1 could be one of the risk factors for intracranial aneurysms in humans.

Summary: Collagen COL22A1 is expressed in perivascular fibroblast-like cells and is required to maintain vascular stability in a zebrafish model. Mutations in COL22A1 are likely to be associated with intracranial aneurysms.

Technical Advances in Single-Cell RNA Sequencing and Applications in Normal and Malignant Hematopoiesis

Single-cell RNA sequencing (scRNA-seq) has been tremendously developed in the past decade owing to overcoming challenges associated with isolation of massive quantities of single cells. Previously, cell heterogeneity and low quantities of available biological material posed significant difficulties to scRNA-seq. Cell-to-cell variation and heterogeneity are fundamental and intrinsic characteristics of normal and malignant hematopoietic cells; this heterogeneity has often been ignored in omics studies. The application of scRNA-seq has profoundly changed our comprehension of many biological phenomena, including organ development and carcinogenesis. Hematopoiesis, is actually a maturation process for more than ten distinct blood and immune cells, and is thought to be critically involved in hematological homeostasis and in sustaining the physiological functions. However, aberrant hematopoiesis directly leads to hematological malignancy, and a deeper understanding of malignant hematopoiesis will provide deeper insights into diagnosis and prognosis for patients with hematological malignancies. Here, we aim to review the recent technical progress and future prospects for scRNA-seq, as applied in physiological and malignant hematopoiesis, in efforts to further understand the hematopoietic hierarchy and to illuminate personalized therapy and precision medicine approaches used in the clinical treatment of hematological malignancies.

RELACS nuclei barcoding enables high-throughput ChIP-seq

Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) is an invaluable tool for mapping chromatin-associated proteins. Current barcoding strategies aim to improve assay throughput and scalability but intense sample handling and lack of standardization over cell types, cell numbers and epitopes hinder wide-spread use in the field. Here, we present a barcoding method to enable high-throughput ChIP-seq using common molecular biology techniques. The method, called RELACS (restriction enzyme-based labeling of chromatin in situ) relies on standardized nuclei extraction from any source and employs chromatin cutting and barcoding within intact nuclei. Barcoded nuclei are pooled and processed within the same ChIP reaction, for maximal comparability and workload reduction. The innovative barcoding concept is particularly user-friendly and suitable for implementation to standardized large-scale clinical studies and scarce samples. Aiming to maximize universality and scalability, RELACS can generate ChIP-seq libraries for transcription factors and histone modifications from hundreds of samples within three days.

Laura Arrigoni et al. present RELACS, a method enabling high-throughput ChIP-seq which involves barcoding and processing intact nuclei in the same ChIP reaction. The method is useful for broad cell types and epitopes, robust to experimental conditions, and drastically decreases workload.

Understanding tumor ecosystems by single-cell sequencing: promises and limitations

Cellular heterogeneity within and across tumors has been a major obstacle in understanding and treating cancer, and the complex heterogeneity is masked if bulk tumor tissues are used for analysis. The advent of rapidly developing single-cell sequencing technologies, which include methods related to single-cell genome, epigenome, transcriptome, and multi-omics sequencing, have been applied to cancer research and led to exciting new findings in the fields of cancer evolution, metastasis, resistance to therapy, and tumor microenvironment. In this review, we discuss recent advances and limitations of these new technologies and their potential applications in cancer studies.

Brain Theranostics and Radiotheranostics: Exosomes and Graphenes In Vivo as Novel Brain Theranostics

Brain disease is one of the greatest threats to public health. Brain theranostics is recently taking shape, indicating the treatments of stroke, inflammatory brain disorders, psychiatric diseases, neurodevelopmental disease, and neurodegenerative disease. However, several factors, such as lack of endophenotype classification, blood-brain barrier (BBB), target determination, ignorance of biodistribution after administration, and complex intercellular communication between brain cells, make brain theranostics application difficult, especially when it comes to clinical application. So, a more thorough understanding of each aspect is needed. In this review, we focus on recent studies regarding the role of exosomes in intercellular communication of brain cells, therapeutic effect of graphene quantum dots, transcriptomics/epitranscriptomics approach for target selection, and in vitro/in vivo considerations.

High-throughput single-cell transcriptomics on organoids

Three-dimensional (3D) tissues grown in culture from human stem cells offer the incredible opportunity to analyze and manipulate human development, and to generate patient-specific models of disease. Methods to sequence DNA and RNA in single cells are being used to analyze these so-called 'organoid' systems in high-resolution. Single-cell transcriptomics has been used to quantitate the similarity of organoid cells to primary tissue counterparts in the brain, intestine, liver, and kidney, as well as identify cell-specific responses to environmental variables and disease conditions. The merging of these two technologies, single-cell genomics and organoids, will have profound impact on personalized medicine in the near future.

Decoding Cell Type Diversity Within the Spinal Cord

To understand fundamental mechanisms of mammalian spinal cord function, it is necessary to reveal the diverse array of constituent spinal "cell types" - populations that can be consistently identified because they share a unique and cohesive set of characteristics. Many parameters can contribute to the definition of a spinal cord cell type, including location, morphology, lineage, electrophysiological properties, circuit features, gene expression patterns, and behavioral contribution. While it is not necessary for all of these features to align completely at all times to identify an individual cell type, a correlation of these characteristics paints a rich portrait of cell identity. This review will summarize recent advances in the identification of mammalian spinal cord neuronal cell types and will highlight the power of transcriptional profiling to identify and characterize the cell types of the spinal cord.

Tumour heterogeneity and metastasis at single-cell resolution

Tumours comprise a heterogeneous collection of cells with distinct genetic and phenotypic properties that can differentially promote progression, metastasis and drug resistance. Emerging single-cell technologies provide a new opportunity to profile individual cells within tumours and investigate what roles they play in these processes. This Review discusses key technological considerations for single-cell studies in cancer, new findings using single-cell technologies and critical open questions for future applications.

Power in Numbers: Single-Cell RNA-Seq Strategies to Dissect Complex Tissues

The growing scale and declining cost of single-cell RNA-sequencing (RNA-seq) now permit a repetition of cell sampling that increases the power to detect rare cell states, reconstruct developmental trajectories, and measure phenotype in new terms such as cellular variance. The characterization of anatomy and developmental dynamics has not had an equivalent breakthrough since groundbreaking advances in live fluorescent microscopy. The new resolution obtained by single-cell RNA-seq is a boon to genetics because the novel description of phenotype offers the opportunity to refine gene function and dissect pleiotropy. In addition, the recent pairing of high-throughput genetic perturbation with single-cell RNA-seq has made practical a scale of genetic screening not previously possible.

Reprogramming the brain with synthetic neurobiology

The mammalian brain is among the most complex organs known in biology. Historically, neuroscience techniques have consisted primarily of low-throughput microscopy and electrophysiological approaches. While these methods will continue to serve the community, the emerging field of synthetic neurobiology may be better equipped to scale with systems neuroscience. By using genetic techniques to achieve cell-type specificity, a map of the connectome, neural activation and recording, and ultimately to program neural development itself, we can begin to build a better framework with which to understand the brain's mechanisms.

Mapping Cellular Reprogramming via Pooled Overexpression Screens with Paired Fitness and Single-Cell RNA-Sequencing Readout

Understanding the effects of genetic perturbations on the cellular state has been challenging using traditional pooled screens, which typically rely on the delivery of a single perturbation per cell and unidimensional phenotypic readouts. Here, we use barcoded open reading frame overexpression libraries coupled with single-cell RNA sequencing to assay cell state and fitness, a technique we call SEUSS (scalable functional screening by sequencing). Using SEUSS, we perturbed hPSCs with a library of developmentally critical transcription factors (TFs) and assayed the impact of TF overexpression on fitness and transcriptomic states. We further leveraged the versatility of the ORF library approach to assay mutant genes and whole gene families. From the transcriptomic responses, we built genetic co-regulatory networks to identify altered gene modules and found that KLF4 and SNAI2 drive opposing effects along the epithelial-mesenchymal transition axis. From the fitness responses, we identified ETV2 as a driver of reprogramming toward an endothelial-like state.

The Transcriptional Regulator SnoN Promotes the Proliferation of Cerebellar Granule Neuron Precursors in the Postnatal Mouse Brain

Control of neuronal precursor cell proliferation is essential for normal brain development, and deregulation of this fundamental developmental event contributes to brain diseases. Typically, neuronal precursor cell proliferation extends over long periods of time during brain development. However, how neuronal precursor proliferation is regulated in a temporally specific manner remains to be elucidated. Here, we report that conditional KO of the transcriptional regulator SnoN in cerebellar granule neuron precursors robustly inhibits the proliferation of these cells and promotes their cell cycle exit at later stages of cerebellar development in the postnatal male and female mouse brain. In laser capture microdissection followed by RNA-Seq, designed to profile gene expression specifically in the external granule layer of the cerebellum, we find that SnoN promotes the expression of cell proliferation genes and concomitantly represses differentiation genes in granule neuron precursors in vivo Remarkably, bioinformatics analyses reveal that SnoN-regulated genes contain binding sites for the transcription factors N-myc and Pax6, which promote the proliferation and differentiation of granule neuron precursors, respectively. Accordingly, we uncover novel physical interactions of SnoN with N-myc and Pax6 in cells. In behavior analyses, conditional KO of SnoN impairs cerebellar-dependent learning in a delayed eye-blink conditioning paradigm, suggesting that SnoN-regulation of granule neuron precursor proliferation bears functional consequences at the organismal level. Our findings define a novel function and mechanism for the major transcriptional regulator SnoN in the control of granule neuron precursor proliferation in the mammalian brain.SIGNIFICANCE STATEMENT This study reports the discovery that the transcriptional regulator SnoN plays a crucial role in the proliferation of cerebellar granule neuron precursors in the postnatal mouse brain. Conditional KO of SnoN in granule neuron precursors robustly inhibits the proliferation of these cells and promotes their cycle exit specifically at later stages of cerebellar development, with biological consequences of impaired cerebellar-dependent learning. Genomics and bioinformatics analyses reveal that SnoN promotes the expression of cell proliferation genes and concomitantly represses cell differentiation genes in vivo Although SnoN has been implicated in distinct aspects of the development of postmitotic neurons, this study identifies a novel function for SnoN in neuronal precursors in the mammalian brain.

Single-Cell Transcriptomics in Cancer Immunobiology: The Future of Precision Oncology

Cancer is a heterogeneous and complex disease. Tumors are formed by cancer cells and a myriad of non-cancerous cell types that together with the extracellular matrix form the tumor microenvironment. These cancer-associated cells and components contribute to shape the progression of cancer and are deeply involved in patient outcome. The immune system is an essential part of the tumor microenvironment, and induction of cancer immunotolerance is a necessary step involved in tumor formation and growth. Immune mechanisms are intimately associated with cancer progression, invasion, and metastasis; as well as to tumor dormancy and modulation of sensitivity to drug therapy. Transcriptome analyses have been extensively used to understand the heterogeneity of tumors, classifying tumors into molecular subtypes and establishing signatures that predict response to therapy and patient outcomes. However, the classification of the tumor cell diversity and specially the identification of rare populations has been limited in these transcriptomic analyses of bulk tumor cell populations. Massively-parallel single-cell RNAseq analysis has emerged as a powerful method to unravel heterogeneity and to study rare cell populations in cancer, through unsupervised sampling and modeling of transcriptional states in single cells. In this context, the study of the role of the immune system in cancer would benefit from single cell approaches, as it will enable the characterization and/or discovery of the cell types and pathways involved in cancer immunotolerance otherwise missed in bulk transcriptomic information. Thus, the analysis of gene expression patterns at single cell resolution holds the potential to provide key information to develop precise and personalized cancer treatment including immunotherapy. This review is focused on the latest single-cell RNAseq methodologies able to agnostically study thousands of tumor cells as well as targeted single-cell RNAseq to study rare populations within tumors. In particular, we will discuss methods to study the immune system in cancer. We will also discuss the current challenges to the study of cancer at the single cell level and the potential solutions to the current approaches.

Revealing the Critical Regulators of Cell Identity in the Mouse Cell Atlas

Recent progress in single-cell technologies has enabled the identification of all major cell types in mouse. However, for most cell types, the regulatory mechanism underlying their identity remains poorly understood. By computational analysis of the recently published mouse cell atlas data, we have identified 202 regulons whose activities are highly variable across different cell types, and more importantly, predicted a small set of essential regulators for each major cell type in mouse. Systematic validation by automated literature and data mining provides strong additional support for our predictions. Thus, these predictions serve as a valuable resource that would be useful for the broad biological community. Finally, we have built a user-friendly, interactive web portal to enable users to navigate this mouse cell network atlas.

Genomic mosaicism in the developing and adult brain

Since the discovery of DNA, the normal developing and functioning brain has been assumed to be composed of cells with identical genomes, which remains the dominant view even today. However, this pervasive assumption is incorrect, as proven by increasing numbers of reports within the last 20 years that have identified multiple forms of somatically produced genomic mosaicism (GM), wherein brain cells-especially neurons-from a single individual show diverse alterations in DNA, distinct from the germline. Critically, these changes alter the actual DNA nucleotide sequences-in contrast to epigenetic mechanisms-and almost certainly contribute to the remarkably diverse phenotypes of single brain cells, including single-cell transcriptomic profiles. Here, we review the history of GM within the normal brain, including its major forms, initiating mechanisms, and possible functions. GM forms include aneuploidies and aneusomies, smaller copy number variations (CNVs), long interspersed nuclear element type 1 (LINE1) repeat elements, and single nucleotide variations (SNVs), as well as DNA content variation (DCV) that reflects all forms of GM with greatest coverage of large, brain cell populations. In addition, technical considerations are examined, along with relationships among GM forms and multiple brain diseases. GM affecting genes and loci within the brain contrast with current neural discovery approaches that rely on sequencing nonbrain DNA (e.g., genome-wide association studies (GWAS)). Increasing knowledge of neural GM has implications for mechanisms of development, diversity, and function, as well as understanding diseases, particularly considering the overwhelming prevalence of sporadic brain diseases that are unlinked to germline mutations. © 2018 The Authors. Developmental Neurobiology Published by Wiley Periodicals, Inc. Develop Neurobiol, 2018.

Large-scale reconstruction of cell lineages using single-cell readout of transcriptomes and CRISPR-Cas9 barcodes by scGESTALT

Lineage relationships among the large number of heterogeneous cell types generated during development are difficult to reconstruct in a high-throughput manner. We recently established a method, scGESTALT, that combines cumulative editing of a lineage barcode array by CRISPR-Cas9 with large-scale transcriptional profiling using droplet-based single-cell RNA sequencing (scRNA-seq). The technique generates edits in the barcode array over multiple timepoints using Cas9 and pools of single-guide RNAs (sgRNAs) introduced during early and late zebrafish embryonic development, which distinguishes it from similar Cas9 lineage-tracing methods. The recorded lineages are captured, along with thousands of cellular transcriptomes, to build lineage trees with hundreds of branches representing relationships among profiled cell types. Here, we provide details for (i) generating transgenic zebrafish; (ii) performing multi-timepoint barcode editing; (iii) building scRNA-seq libraries from brain tissue; and (iv) concurrently amplifying lineage barcodes from captured single cells. Generating transgenic lines takes 6 months, and performing barcode editing and generating single-cell libraries involve 7 d of hands-on time. scGESTALT provides a scalable platform to map lineage relationships between cell types in any system that permits genome editing during development, regeneration, or disease.

Multi-omics at single-cell resolution: comparison of experimental and data fusion approaches

Biological samples are inherently heterogeneous and complex. Tackling this complexity requires innovative technological and analytical solutions. Recent advances in high-throughput single-cell isolation and nucleic acid barcoding methods are rapidly changing the technological landscape of biological sciences and now make it possible to measure the (epi)genomic, transcriptomic, or proteomic state of individual cells. In addition, few experimental approaches enable multi-omics measurements of the same cell. However, merging-omics data collected from different experiments remains a considerable challenge. Although several strategies for merging transcriptomics datasets have recently been introduced, cell-to-cell variability and heterogeneity remains one of the confounding factors limiting data fusion and integration. Here, we focus our discussion on the latest single-cell technological and analytical solutions to achieve high data dimensionality and resolution. Obtaining datasets with a wealth of multi-omics information will undoubtedly provide new avenues for researchers to unravel the complexity of biological samples encountered in modern biological research and molecular diagnostics.

A Single-Cell Sequencing Guide for Immunologists

In recent years there has been a rapid increase in the use of single-cell sequencing (scRNA-seq) approaches in the field of immunology. With the wide range of technologies available, it is becoming harder for users to select the best scRNA-seq protocol/platform to address their biological questions of interest. Here, we compared the advantages and limitations of four commonly used scRNA-seq platforms in order to clarify their suitability for different experimental applications. We also address how the datasets generated by different scRNA-seq platforms can be integrated, and how to identify unknown populations of single cells using unbiased bioinformatics methods.

Single-cell RNA sequencing of the mammalian pineal gland identifies two pinealocyte subtypes and cell type-specific daily patterns of gene expression

The vertebrate pineal gland is dedicated to the production of the hormone melatonin, which increases at night to influence circadian and seasonal rhythms. This increase is associated with dramatic changes in the pineal transcriptome. Here, single-cell analysis of the rat pineal transcriptome was approached by sequencing mRNA from ~17,000 individual pineal cells, with the goals of profiling the cells that comprise the pineal gland and examining the proposal that there are two distinct populations of pinealocytes differentiated by the expression of Asmt, which encodes the enzyme that converts N-acetylserotonin to melatonin. In addition, this analysis provides evidence of cell-specific time-of-day dependent changes in gene expression. Nine transcriptomically distinct cell types were identified: ~90% were classified as melatonin-producing α- and β-pinealocytes (1:19 ratio). Non-pinealocytes included three astrocyte subtypes, two microglia subtypes, vascular and leptomeningeal cells, and endothelial cells. α-Pinealocytes were distinguished from β-pinealocytes by ~3-fold higher levels of Asmt transcripts. In addition, α-pinealocytes have transcriptomic differences that likely enhance melatonin formation by increasing the availability of the Asmt cofactor S-adenosylmethionine, resulting from increased production of a precursor of S-adenosylmethionine, ATP. These transcriptomic differences include ~2-fold higher levels of the ATP-generating oxidative phosphorylation transcriptome and ~8-fold lower levels of the ribosome transcriptome, which is expected to reduce the consumption of ATP by protein synthesis. These findings suggest that α-pinealocytes have a specialized role in the pineal gland: efficiently O-methylating the N-acetylserotonin produced and released by β-pinealocytes, thereby improving the overall efficiency of melatonin synthesis. We have also identified transcriptomic changes that occur between night and day in seven cell types, the majority of which occur in β-pinealocytes and to a lesser degree in α-pinealocytes; many of these changes were mimicked by adrenergic stimulation with isoproterenol. The cellular heterogeneity of the pineal gland as revealed by this study provides a new framework for understanding pineal cell biology at single-cell resolution.

Electrical Lysis and RNA Extraction from Single Cells Fixed by Dithiobis(succinimidyl propionate)

We present a microfluidic method for electrical lysis and RNA extraction from single fixed cells leveraging reversible cross-linker dithiobis(succinimidyl propionate) (DSP). Our microfluidic system captures a single DSP-fixed cell at a hydrodynamic trap, reverse-cross-links the DSP molecules on a chip with dithiothreitol, lyses the plasma membrane via electrical field, and extracts cytoplasmic RNA with isotachophoresis-aided nucleic acids extraction. All of the on-chip processes complete in less than 5 min. We demonstrated the method using K562 leukemia cells and benchmarked the performance of RNA extraction with reverse transcription quantitative polymerase chain reaction. We also demonstrated the integration of our method with single-cell RNA sequencing.

Neuro-evo-devo in the single cell sequencing era

The nervous system represents the most complex tissue in animals. How this complexity evolved has been a challenging question to address. The explosion in single cell sequencing techniques, the development of new algorithms to cluster single cells into cell types, along with powerful tools for drawing developmental trajectories offer a unique opportunity to compare homologous cell types between species. They further permit the identification of key developmental points and transcription factors that can lead to the evolution of new cell types. At the same time, the ease of use and efficiency of CRISPR genome editing technology allow validation of predicted regulators. This promises exciting developments in the next few years in the field of neuronal evolution and development.

A Single-Cell Transcriptional Atlas of the Developing Murine Cerebellum

The cerebellum develops from a restricted number of cell types that precisely organize to form the circuitry that controls sensory-motor coordination and some higher-order cognitive processes. To acquire an enhanced understanding of the molecular processes that mediate cerebellar development, we performed single-cell RNA-sequencing of 39,245 murine cerebellar cells at twelve critical developmental time points. Using recognized lineage markers, we confirmed that the single-cell data accurately recapitulate cerebellar development. We then followed distinct populations from emergence through migration and differentiation, and determined the associated transcriptional cascades. After identifying key lineage commitment decisions, focused analyses uncovered waves of transcription factor expression at those branching points. Finally, we created Cell Seek, a flexible online interface that facilitates exploration of the dataset. Our study provides a transcriptional summarization of cerebellar development at single-cell resolution that will serve as a valuable resource for future investigations of cerebellar development, neurobiology, and disease.

scClustViz - Single-cell RNAseq cluster assessment and visualization

Single-cell RNA sequencing (scRNAseq) represents a new kind of microscope that can measure the transcriptome profiles of thousands of individual cells from complex cellular mixtures, such as in a tissue, in a single experiment. This technology is particularly valuable for characterization of tissue heterogeneity because it can be used to identify and classify all cell types in a tissue. This is generally done by clustering the data, based on the assumption that cells of a particular type share similar transcriptomes, distinct from other cell types in the tissue. However, nearly all clustering algorithms have tunable parameters which affect the number of clusters they will identify in data.

The R Shiny software tool described here, scClustViz, provides a simple interactive graphical user interface for exploring scRNAseq data and assessing the biological relevance of clustering results. Given that cell types are expected to have distinct gene expression patterns, scClustViz uses differential gene expression between clusters as a metric for assessing the fit of a clustering result to the data at multiple cluster resolution levels. This helps select a clustering parameter for further analysis. scClustViz also provides interactive visualisation of: cluster-specific distributions of technical factors, such as predicted cell cycle stage and other metadata; cluster-wise gene expression statistics to simplify annotation of cell types and identification of cell type specific marker genes; and gene expression distributions over all cells and cell types.

scClustViz provides an interactive interface for visualisation, assessment, and biological interpretation of cell-type classifications in scRNAseq experiments that can be easily added to existing analysis pipelines, enabling customization by bioinformaticians while enabling biologists to explore their results without the need for computational expertise. It is available at https://baderlab.github.io/scClustViz/.

scClustViz - Single-cell RNAseq cluster assessment and visualization

Single-cell RNA sequencing (scRNAseq) represents a new kind of microscope that can measure the transcriptome profiles of thousands of individual cells from complex cellular mixtures, such as in a tissue, in a single experiment. This technology is particularly valuable for characterization of tissue heterogeneity because it can be used to identify and classify all cell types in a tissue. This is generally done by clustering the data, based on the assumption that cells of a particular type share similar transcriptomes, distinct from other cell types in the tissue. However, nearly all clustering algorithms have tunable parameters which affect the number of clusters they will identify in data. The R Shiny software tool described here, scClustViz, provides a simple interactive graphical user interface for exploring scRNAseq data and assessing the biological relevance of clustering results. Given that cell types are expected to have distinct gene expression patterns, scClustViz uses differential gene expression between clusters as a metric for assessing the fit of a clustering result to the data at multiple cluster resolution levels. This helps select a clustering parameter for further analysis. scClustViz also provides interactive visualisation of: cluster-specific distributions of technical factors, such as predicted cell cycle stage and other metadata; cluster-wise gene expression statistics to simplify annotation of cell types and identification of cell type specific marker genes; and gene expression distributions over all cells and cell types. scClustViz provides an interactive interface for visualisation, assessment, and biological interpretation of cell-type classifications in scRNAseq experiments that can be easily added to existing analysis pipelines, enabling customization by bioinformaticians while enabling biologists to explore their results without the need for computational expertise. It is available at https://baderlab.github.io/scClustViz/.

Lead Modulates trans- and cis-Expression Quantitative Trait Loci (eQTLs) in Drosophila melanogaster Heads

Lead exposure has long been one of the most important topics in global public health because it is a potent developmental neurotoxin. Here, an eQTL analysis, which is the genome-wide association analysis of genetic variants with gene expression, was performed. In this analysis, the male heads of 79 Drosophila melanogaster inbred lines from Drosophila Synthetic Population Resource (DSPR) were treated with or without developmental exposure, from hatching to adults, to 250 μM lead acetate [Pb(C₂H₃O₂)₂]. The goal was to identify genomic intervals that influence the gene-expression response to lead. After detecting 1798 cis-eQTLs and performing an initial trans-eQTL analysis, we focused our analysis on lead-sensitive "trans-eQTL hotspots," defined as genomic regions that are associated with a cluster of genes in a lead-dependent manner. We noticed that the genes associated with one of the 14 detected trans-eQTL hotspots, Chr 2L: 6,250,000 could be roughly divided into two groups based on their differential expression profile patterns and different categories of function. This trans-eQTL hotspot validates one identified in a previous study using different recombinant inbred lines. The expression of all the associated genes in the trans-eQTL hotspot was visualized with hierarchical clustering analysis. Besides the overall expression profile patterns, the heatmap displayed the segregation of differential parental genetic contributions. This suggested that trans-regulatory regions with different genetic contributions from the parental lines have significantly different expression changes after lead exposure. We believe this study confirms our earlier study, and provides important insights to unravel the genetic variation in lead susceptibility in Drosophila model.

Revealing cellular and molecular complexity of the central nervous system using single cell sequencing

The mammalian central nervous system (CNS) is one of the most complex systems, with thousands of cell types and subtypes with distinct and unique morphology and gene expression profiles. Based on classic histological methods and conventional cellular and molecular approaches, single cell sequencing is becoming a powerful tool to uncover the complexity of the CNS. In this review, we summarize the principle of single cell sequencing and highlight its use for studying the development of neural stem cells, neural progenitors, and distinct neurons. By revealing transcriptomes in each individual cell using single cell sequencing, we are now able to dissect the cellular heterogeneity of a hundred billion cells in the CNS and comprehensively investigate mechanisms of brain development and function at the cellular and molecular levels.

Understanding the Biology and Pathogenesis of the Kidney by Single-Cell Transcriptomic Analysis

Background

Single-cell RNA-seq (scRNA-seq) has recently emerged as a revolutionary and powerful tool for biomedical research. However, there have been relatively few studies using scRNA-seq in the field of kidney study.

Summary

scRNA-seq achieves gene expression profiling at single-cell resolution in contrast with the conventional methods of gene expression profiling, which are based on cell population and give averaged values of gene expression of the cells. Single-cell transcriptomic analysis is crucial because individual cells of the same type are highly heterogeneous in gene expression, which reflects the existence of subpopulations, different cellular states, or molecular dynamics, of the cells, and should be resolved for further insights. In addition, gene expression analysis of tissues or organs that usually comprise multiple cell types or subtypes results in data that are not fully applicable to any given cell type. scRNA-seq is capable of identifying all cell types and subtypes in a tissue, including those that are new or present in small quantity. With these unique capabilities, scRNA-seq has been used to dissect molecular processes in cell differentiation and to trace cell lineages in development. It is also used to analyze the cells in a lesion of disease to identify the cell types and molecular dynamics implicated in the injury. With continuous technical improvement, scRNA-seq has become extremely high throughput and cost effective, making it accessible to all laboratories. In the present review article, we provide an overall review of scRNA-seq concerning its history, improvements, and applications. In addition, we describe the available studies in which scRNA-seq was employed in the field of kidney research. Lastly, we discuss other potential uses of scRNA-seq for kidney research.

Key Message

This review article provides general information on scRNA-seq and its various uses. Particularly, we summarize the studies in the field of kidney diseases in which scRNA-seq was used and discuss potential additional uses of scRNA-seq for kidney research.

Estimating the frequency of multiplets in single-cell RNA sequencing from cell-mixing experiments

In single-cell RNA-sequencing, it is important to know the frequency at which the sequenced transcriptomes actually derive from multiple cells. A common method to estimate this multiplet frequency is to mix two different types of cells (e.g., human and mouse), and then determine how often the transcriptomes contain transcripts from both cell types. When the two cell types are mixed in equal proportion, the calculation of the multiplet frequency from the frequency of mixed transcriptomes is straightforward. But surprisingly, there are no published descriptions of how to calculate the multiplet frequency in the general case when the cell types are mixed unequally. Here, I derive equations to analytically calculate the multiplet frequency from the numbers of observed pure and mixed transcriptomes when two cell types are mixed in arbitrary proportions, under the assumption that the loading of cells into droplets or wells is Poisson.

Single-cell genomics to guide human stem cell and tissue engineering

To understand human development and disease, as well as to regenerate damaged tissues, scientists are working to engineer certain cell types in vitro and to create 3D microenvironments in which cells behave physiologically. Single-cell genomics (SCG) technologies are being applied to primary human organs and to engineered cells and tissues to generate atlases of cell diversity in these systems at unparalleled resolution. Moving beyond atlases, SCG methods are powerful tools for gaining insight into the engineering and disease process. Here we discuss how scientists can use single-cell sequencing to optimize human cell and tissue engineering by measuring precision, detecting inefficiencies, and assessing accuracy. We also provide a perspective on how emerging SCG methods can be used to reverse-engineer human cells and tissues and unravel disease mechanisms.

NeuroSystematics and Periodic System of Neurons: Model vs Reference Species at Single-Cell Resolution

There is more than one way to develop neuronal complexity, and animals frequently use different molecular toolkits to achieve similar functional outcomes (=convergent evolution). Neurons are different not only because they have different functions, but also because neurons and circuits have different genealogies, and perhaps independent origins at the broadest scale from ctenophores and cnidarians to cephalopods and primates. By combining modern phylogenomics, single-neuron sequencing (scRNA-seq), machine learning, single-cell proteomics, and metabolomic across Metazoa, it is possible to reconstruct the evolutionary histories of neurons tracing them to ancestral secretory cells. Comparative data suggest that neurons, and perhaps synapses, evolved at least 2-3 times (in ctenophore, cnidarian and bilateral lineages) during ∼600 million years of animal evolution. There were also several independent events of the nervous system centralization either from a common bilateral/cnidarian ancestor without the bona fide neurons or from the urbilaterian with diffuse, nerve-net type nervous system. From the evolutionary standpoint, (i) a neuron should be viewed as a functional rather than a genetic character, and (ii) any given neural system might be chimeric and composed of different cell lineages with distinct origins and evolutionary histories. The identification of distant neural homologies or examples of convergent evolution among 34 phyla will not only allow the reconstruction of neural systems' evolution but together with single-cell "omic" approaches the proposed synthesis would lead to the "Periodic System of Neurons" with predictive power for neuronal phenotypes and plasticity. Such a phylogenetic classification framework of Neuronal Systematics (NeuroSystematics) might be a conceptual analog of the Periodic System of Chemical Elements. scRNA-seq profiling of all neurons in an entire brain or Brain-seq is now fully achievable in many nontraditional reference species across the entire animal kingdom. Arguably, marine animals are the most suitable for the proposed tasks because the world oceans represent the greatest taxonomic and body-plan diversity.

zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs

Background

Single-cell RNA-sequencing (scRNA-seq) experiments typically analyze hundreds or thousands of cells after amplification of the cDNA. The high throughput is made possible by the early introduction of sample-specific bar codes (BCs), and the amplification bias is alleviated by unique molecular identifiers (UMIs). Thus, the ideal analysis pipeline for scRNA-seq data needs to efficiently tabulate reads according to both BC and UMI.

Findings

zUMIs is a pipeline that can handle both known and random BCs and also efficiently collapse UMIs, either just for exon mapping reads or for both exon and intron mapping reads. If BC annotation is missing, zUMIs can accurately detect intact cells from the distribution of sequencing reads. Another unique feature of zUMIs is the adaptive downsampling function that facilitates dealing with hugely varying library sizes but also allows the user to evaluate whether the library has been sequenced to saturation. To illustrate the utility of zUMIs, we analyzed a single-nucleus RNA-seq dataset and show that more than 35% of all reads map to introns. Also, we show that these intronic reads are informative about expression levels, significantly increasing the number of detected genes and improving the cluster resolution.

Conclusions

zUMIs flexibility makes if possible to accommodate data generated with any of the major scRNA-seq protocols that use BCs and UMIs and is the most feature-rich, fast, and user-friendly pipeline to process such scRNA-seq data.

Specialized Subpopulations of Deep-Layer Pyramidal Neurons in the Neocortex: Bridging Cellular Properties to Functional Consequences

Neocortical pyramidal neurons with somata in layers 5 and 6 are among the most visually striking and enigmatic neurons in the brain. These deep-layer pyramidal neurons (DLPNs) integrate a plethora of cortical and extracortical synaptic inputs along their impressive dendritic arbors. The pattern of cortical output to both local and long-distance targets is sculpted by the unique physiological properties of specific DLPN subpopulations. Here we revisit two broad DLPN subpopulations: those that send their axons within the telencephalon (intratelencephalic neurons) and those that project to additional target areas outside the telencephalon (extratelencephalic neurons). While neuroscientists across many subdisciplines have characterized the intrinsic and synaptic physiological properties of DLPN subpopulations, our increasing ability to selectively target and manipulate these output neuron subtypes advances our understanding of their distinct functional contributions. This Viewpoints article summarizes our current knowledge about DLPNs and highlights recent work elucidating the functional differences between DLPN subpopulations.

Interpretable dimensionality reduction of single cell transcriptome data with deep generative models

Single-cell RNA-sequencing has great potential to discover cell types, identify cell states, trace development lineages, and reconstruct the spatial organization of cells. However, dimension reduction to interpret structure in single-cell sequencing data remains a challenge. Existing algorithms are either not able to uncover the clustering structures in the data or lose global information such as groups of clusters that are close to each other. We present a robust statistical model, scvis, to capture and visualize the low-dimensional structures in single-cell gene expression data. Simulation results demonstrate that low-dimensional representations learned by scvis preserve both the local and global neighbor structures in the data. In addition, scvis is robust to the number of data points and learns a probabilistic parametric mapping function to add new data points to an existing embedding. We then use scvis to analyze four single-cell RNA-sequencing datasets, exemplifying interpretable two-dimensional representations of the high-dimensional single-cell RNA-sequencing data.

Single-Cell Transcriptomics Meets Lineage Tracing

Reconstructing lineage relationships between cells within a tissue or organism is a long-standing aim in biology. Traditionally, lineage tracing has been achieved through the (genetic) labeling of a cell followed by the tracking of its offspring. Currently, lineage trajectories can also be predicted using single-cell transcriptomics. Although single-cell transcriptomics provides detailed phenotypic information, the predicted lineage trajectories do not necessarily reflect genetic relationships. Recently, techniques have been developed that unite these strategies. In this Review, we discuss transcriptome-based lineage trajectory prediction algorithms, single-cell genetic lineage tracing, and the promising combination of these techniques for stem cell and cancer research.