Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons

Clustering single cells: a review of approaches on high-and low-depth single-cell RNA-seq data

Advances in single-cell RNA-sequencing technology have resulted in a wealth of studies aiming to identify transcriptomic cell types in various biological systems. There are multiple experimental approaches to isolate and profile single cells, which provide different levels of cellular and tissue coverage. In addition, multiple computational strategies have been proposed to identify putative cell types from single-cell data. From a data generation perspective, recent single-cell studies can be classified into two groups: those that distribute reads shallowly over large numbers of cells and those that distribute reads more deeply over a smaller cell population. Although there are advantages to both approaches in terms of cellular and tissue coverage, it is unclear whether different computational cell type identification methods are better suited to one or the other experimental paradigm. This study reviews three cell type clustering algorithms, each representing one of three broad approaches, and finds that PCA-based algorithms appear most suited to low read depth data sets, whereas gene clustering-based and biclustering algorithms perform better on high read depth data sets. In addition, highly related cell classes are better distinguished by higher-depth data, given the same total number of reads; however, simultaneous discovery of distinct and similar types is better served by lower-depth, higher cell number data. Overall, this study suggests that the depth of profiling should be determined by initial assumptions about the diversity of cells in the population, and that the selection of clustering algorithm(s) subsequently based on the depth of profiling will allow for better identification of putative transcriptomic cell types.

RNA Profiling of the Human and Mouse Spinal Cord Stem Cell Niches Reveals an Embryonic-like Regionalization with MSX1+ Roof-Plate-Derived Cells

Anamniotes, rodents, and young humans maintain neural stem cells in the ependymal zone (EZ) around the central canal of the spinal cord, representing a possible endogenous source for repair in mammalian lesions. Cell diversity and genes specific for this region are ill defined. A cellular and molecular resource is provided here for the mouse and human EZ based on RNA profiling, immunostaining, and fluorescent transgenic mice. This uncovered the conserved expression of 1,200 genes including 120 transcription factors. Unexpectedly the EZ maintains an embryonic-like dorsal-ventral pattern of expression of spinal cord developmental transcription factors (ARX, FOXA2, MSX1, and PAX6). In mice, dorsal and ventral EZ cells express Vegfr3 and are derived from the embryonic roof and floor plates. The dorsal EZ expresses a high level of Bmp6 and Gdf10 genes and harbors a subpopulation of radial quiescent cells expressing MSX1 and ID4 transcription factors.

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A molecular resource for the human and mouse spinal cord ependymal zone

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Identification of 120 transcription factors in the human and mouse ependymal zone

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Embryonic-like organization of the adult spinal cord ependymal zone

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Dorsal ependymal cells expressing Msx1 are derived from the embryonic roof plate

Single-Cell RNA Sequencing to Understand Host-Pathogen Interactions

Host-pathogen interactions, particularly in the context of bacterial infections, are dynamic exchanges where transcriptional heterogeneity from both the host and the pathogen can lead to many diverse outcomes via distinct molecular pathways. Transcriptional profiling at the single-cell level, on a genome-wide scale, has enabled a greater appreciation of the cellular diversity in complex biological organisms and the myriad of host transcriptional states during infection. Here, we highlight recent reports of single-cell RNA sequencing within the context of host-pathogen interactions, describe current limitations for detecting and profiling the transcriptome of invading pathogens at the single-cell level, and suggest exciting future prospects for this technology in the study of infection. We propose that understanding infection as an integrated process between pathogen and host with resolution at the single-cell level will ultimately inform development of vaccines with greater productive and protective host immunity, enable the development of novel therapeutics that harness host mechanisms, and yield more accurate biomarkers to guide better diagnostics.

Deciphering Human Tumor Biology by Single-Cell Expression Profiling

Human tumors are complex ecosystems where diverse cancer and noncancer cells interact to determine tumor biology and response to therapies. Genomic and transcriptomic methods have traditionally profiled these intricate ecosystems as bulk samples, thereby masking individual cellular programs and the variability among them. Recent advances in single-cell profiling have paved the way for studying tumors at the resolution of individual cells, providing a compelling strategy to bridge gaps in our understanding of human tumors. Here, we review methodologies for single-cell expression profiling of tumors and the initial studies deploying them in clinical contexts. We highlight how these studies uncover new biology and provide insights into drug resistance, stem cell programs, metastasis, and tumor classifications. We also discuss areas of technology development in single-cell genomics that provide new tools to address key questions in cancer biology. These emerging studies and technologies have the potential to revolutionize our understanding and management of human malignancies.

Dissecting human gliomas by single-cell RNA sequencing

Diffuse gliomas are the most common human primary brain tumors and remain incurable. They are complex entities in which diverse genetic and nongenetic effects determine tumor biology and clinical course. Our current understanding of gliomas in patients is primarily based on genomic and transcriptomic methods that have profiled them as bulk, providing critical information yet masking the diversity of cells within each tumor. Recent advances in single-cell DNA and RNA profiling have paved the way to studying tumors at cellular resolution. Here, we review initial studies deploying single-cell analysis in clinical glioma samples, with a focus on RNA expression profiling. We highlight how these studies provide new insights into glioma biology, tumor heterogeneity, cancer cell lineages, cancer stem cell programs, the tumor microenvironment, and glioma classification.

Wnt produced by stretched roof-plate cells is required for the promotion of cell proliferation around the central canal of the spinal cord

Cell morphology changes dynamically during embryogenesis, and these changes create new interactions with surrounding cells, some of which are presumably mediated by intercellular signaling. However, the effects of morphological changes on intercellular signaling remain to be fully elucidated. In this study, we examined the effect of morphological changes in Wnt-producing cells on intercellular signaling in the spinal cord. After mid-gestation, roof-plate cells stretched along the dorsoventral axis in the mouse spinal cord, resulting in new contact at their tips with the ependymal cells that surround the central canal. Wnt1 and Wnt3a were produced by the stretched roof-plate cells and delivered to the cell process tip. Whereas Wnt signaling was activated in developing ependymal cells, Wnt activation in dorsal ependymal cells, which were close to the stretched roof plate, was significantly suppressed in embryos with roof plate-specific conditional knockout of Wls, which encodes a factor that is essential for Wnt secretion. Furthermore, proliferation of these cells was impaired in Wls conditional knockout mice during development and after induced spinal cord injury in adults. Therefore, morphological changes in Wnt-producing cells appear to generate new Wnt signal targets.

Single cell transcriptomics: moving towards multi-omics

Single-cell multi-omics analysis helps characterize multiple layers of molecular features at a single-cell scale to provide insights into cellular processes and functions.

Identification of Cell Types from Single-Cell Transcriptomic Data

Unprecedented technological advances in single-cell RNA-sequencing (scRNA-seq) technology have now made it possible to profile genome-wide expression in single cells at low cost and high throughput. There is substantial ongoing effort to use scRNA-seq measurements to identify the "cell types" that form components of a complex tissue, akin to taxonomizing species in ecology. Cell type classification from scRNA-seq data involves the application of computational tools rooted in dimensionality reduction and clustering, and statistical analysis to identify molecular signatures that are unique to each type. As datasets continue to grow in size and complexity, computational challenges abound, requiring analytical methods to be scalable, flexible, and robust. Moreover, careful consideration needs to be paid to experimental biases and statistical challenges that are unique to these measurements to avoid artifacts. This chapter introduces these topics in the context of cell-type identification, and outlines an instructive step-by-step example bioinformatic pipeline for researchers entering this field.

Rare Cell Type Detection

High-throughput single-cell technologies have great potential to discover new cell types. Here, we present a novel computational method, called GiniClust (Jiang et al., Genome Biol 17(1):144, 2016), to overcome the challenge of detecting rare cell types that are distinct from a large population.

Single-Cell RNA-Seq by Multiple Annealing and Tailing-Based Quantitative Single-Cell RNA-Seq (MATQ-Seq)

Single-cell technologies have emerged as advanced tools to study various biological processes that demand the single cell resolution. To detect subtle heterogeneity in the transcriptome, high accuracy and sensitivity are still desired for single-cell RNA-seq. We describe here multiple annealing and dC-tailing-based quantitative single-cell RNA-seq (MATQ-seq) with ~90% capture efficiency. In addition, MATQ-seq is a total RNA assay allowing for detection of nonpolyadenylated transcripts.

Single-Cell Genomics: A Stepping Stone for Future Immunology Discoveries

The immunology field has invested great efforts and ingenuity to characterize the various immune cell types and elucidate their functions. However, accumulating evidence indicates that current technologies and classification schemes are limited in their ability to account for the functional heterogeneity of immune processes. Single-cell genomics hold the potential to revolutionize the way we characterize complex immune cell assemblies and study their spatial organization, dynamics, clonal distribution, pathways, function, and crosstalks. In this Perspective, we consider recent and forthcoming technological and analytical advances in single-cell genomics and the potential impact of those advances on the future of immunology research and immunotherapy.

Single-nucleus and single-cell transcriptomes compared in matched cortical cell types

Transcriptomic profiling of complex tissues by single-nucleus RNA-sequencing (snRNA-seq) affords some advantages over single-cell RNA-sequencing (scRNA-seq). snRNA-seq provides less biased cellular coverage, does not appear to suffer cell isolation-based transcriptional artifacts, and can be applied to archived frozen specimens. We used well-matched snRNA-seq and scRNA-seq datasets from mouse visual cortex to compare cell type detection. Although more transcripts are detected in individual whole cells (~11,000 genes) than nuclei (~7,000 genes), we demonstrate that closely related neuronal cell types can be similarly discriminated with both methods if intronic sequences are included in snRNA-seq analysis. We estimate that the nuclear proportion of total cellular mRNA varies from 20% to over 50% for large and small pyramidal neurons, respectively. Together, these results illustrate the high information content of nuclear RNA for characterization of cellular diversity in brain tissues.

Achieving Automated Organelle Biopsy on Small Single Cells Using a Cell Surgery Robotic System

Single cell surgery such as manipulation or removal of subcellular components or/and organelles from single cells is increasingly used for the study of diseases and their causes in precision medicine. This paper presents a robotic surgery system to achieve automated organelle biopsy of single cells with dimensions of less than 20 μm in diameter. The complexity of spatial detection of the organelle position is reduced by patterning the cells using a microfluidic chip device. A sliding mode nonlinear controller is developed to enable extraction of organelles, such as the mitochondria and the nucleus, from single cells with high precision. An image processing algorithm is also developed to automatically detect the position of the desired organelle. The effectiveness of the proposed robotic surgery system is demonstrated experimentally with automated extraction of mitochondria and nucleus from human acute promyelocytic leukemia cells and human fibroblast cells. Extraction is followed by biological tests to indicate the functionality of biopsied mitochondria as well as the cell viability after removal of mitochondria. The results presented here have revealed that the proposed approach of automated organelle biopsy on single small cells is feasible.

Prenatal Alcohol Exposure: Profiling Developmental DNA Methylation Patterns in Central and Peripheral Tissues

Background: Prenatal alcohol exposure (PAE) can alter the development of neurobiological systems, leading to lasting neuroendocrine, neuroimmune, and neurobehavioral deficits. Although the etiology of this reprogramming remains unknown, emerging evidence suggests DNA methylation as a potential mediator and biomarker for the effects of PAE due to its responsiveness to environmental cues and relative stability over time. Here, we utilized a rat model of PAE to examine the DNA methylation profiles of rat hypothalami and leukocytes at four time points during early development to assess the genome-wide impact of PAE on the epigenome and identify potential biomarkers of PAE. Our model of PAE resulted in blood alcohol levels of ~80-150 mg/dl throughout the equivalent of the first two trimesters of human pregnancy. Hypothalami were analyzed on postnatal days (P) 1, 8, 15, 22 and leukocytes at P22 to compare central and peripheral markers. Genome-wide DNA methylation analysis was performed by methylated DNA immunoprecipitation followed by next-generation sequencing. Results: PAE resulted in lasting changes to DNA methylation profiles across all four ages, with 118 differentially methylated regions (DMRs) displaying persistent alterations across the developmental period at a false-discovery rate (FDR) < 0.05. In addition, 299 DMRs showed the same direction of change in the hypothalamus and leukocytes of P22 pups at an FDR < 0.05, with some genes overlapping with the developmental profile findings. The majority of these DMRs were located in intergenic regions, which contained several computationally-predicted transcription factor binding sites. Differentially methylated genes were generally involved in immune function, epigenetic remodeling, metabolism, and hormonal signaling, as determined by gene ontology analyses. Conclusions: Persistent DNA methylation changes in the hypothalamus may be associated with the long-term physiological and neurobehavioral alterations in observed in PAE. Furthermore, correlations between epigenetic alterations in peripheral tissues and those in the brain will provide a foundation for the development of biomarkers of fetal alcohol spectrum disorder (FASD). Finally, findings from studies of PAE provide important insight into the etiology of neurodevelopmental and mental health disorders, as they share numerous phenotypes and comorbidities.

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.

Transcriptional trajectories of human kidney injury progression

BACKGROUND

The molecular understanding of the progression from acute to chronic organ injury is limited. Ischemia/reperfusion injury (IRI) triggered during kidney transplantation can contribute to progressive allograft dysfunction.

METHODS

Protocol biopsies (n = 163) were obtained from 42 kidney allografts at 4 time points after transplantation. RNA sequencing-mediated (RNA-seq-mediated) transcriptional profiling and machine learning computational approaches were employed to analyze the molecular responses to IRI and to identify shared and divergent transcriptional trajectories associated with distinct clinical outcomes. The data were compared with the response to IRI in a mouse model of the acute to chronic kidney injury transition.

RESULTS

In the first hours after reperfusion, all patients exhibited a similar transcriptional program under the control of immediate-early response genes. In the following months, we identified 2 main transcriptional trajectories leading to kidney recovery or to sustained injury with associated fibrosis and renal dysfunction. The molecular map generated by this computational approach highlighted early markers of kidney disease progression and delineated transcriptional programs associated with the transition to chronic injury. The characterization of a similar process in a mouse IRI model extended the relevance of our findings beyond transplantation.

CONCLUSIONS

The integration of multiple transcriptomes from serial biopsies with advanced computational algorithms overcame the analytical hurdles related to variability between individuals and identified shared transcriptional elements of kidney disease progression in humans, which may prove as useful predictors of disease progression following kidney transplantation and kidney injury. This generally applicable approach opens the way for an unbiased analysis of human disease progression.

FUNDING

The study was supported by the California Institute for Regenerative Medicine and by the Swiss National Science Foundation.

T Helper Cell Cytokines Modulate Intestinal Stem Cell Renewal and Differentiation

In the small intestine, a niche of accessory cell types supports the generation of mature epithelial cell types from intestinal stem cells (ISCs). It is unclear, however, if and how immune cells in the niche affect ISC fate or the balance between self-renewal and differentiation. Here, we use single-cell RNA sequencing (scRNA-seq) to identify MHC class II (MHCII) machinery enrichment in two subsets of Lgr5+ ISCs. We show that MHCII+ Lgr5+ ISCs are non-conventional antigen-presenting cells in co-cultures with CD4+ T helper (Th) cells. Stimulation of intestinal organoids with key Th cytokines affects Lgr5+ ISC renewal and differentiation in opposing ways: pro-inflammatory signals promote differentiation, while regulatory cells and cytokines reduce it. In vivo genetic perturbation of Th cells or MHCII expression on Lgr5+ ISCs impacts epithelial cell differentiation and IEC fate during infection. These interactions between Th cells and Lgr5+ ISCs, thus, orchestrate tissue-wide responses to external signals.

Co-expression in Single-Cell Analysis: Saving Grace or Original Sin?

As a fundamental unit of life, the cell has rightfully been the subject of intense investigation throughout the history of biology. Technical innovations now make it possible to assay cellular features at genomic scale, yielding breakthroughs in our understanding of the molecular organization of tissues, and even whole organisms. As these data accumulate we will soon be faced with a new challenge: making sense of the plethora of results. Early investigations into the replicability of cell type profiles inferred from single-cell RNA sequencing data have indicated that this is likely to be surprisingly straightforward due to consistent gene co-expression. In this opinion article we discuss the evidence for this claim and its implications for interpreting cell type-specific gene expression.

Adult Hippocampal Neurogenesis: A Coming-of-Age Story

What has become standard textbook knowledge over the last decade was a hotly debated matter a decade earlier: the proposition that new neurons are generated in the adult mammalian CNS. The early discovery by Altman and colleagues in the 1960s was vulnerable to criticism due to the lack of technical strategies for unequivocal demonstration, quantification, and physiological analysis of newly generated neurons in adult brain tissue. After several technological advancements had been made in the field, we published a paper in 1996 describing the generation of new neurons in the adult rat brain and the decline of hippocampal neurogenesis during aging. The paper coincided with the publication of several other studies that together established neurogenesis as a cellular mechanism in the adult mammalian brain. In this Progressions article, which is by no means a comprehensive review, we recount our personal view of the initial setting that led to our study and we discuss some of its implications and developments that followed. We also address questions that remain regarding the regulation and function of neurogenesis in the adult mammalian brain, in particular the existence of neurogenesis in the adult human brain.

The subiculum is a patchwork of discrete subregions

In the hippocampus, the classical pyramidal cell type of the subiculum acts as a primary output, conveying hippocampal signals to a diverse suite of downstream regions. Accumulating evidence suggests that the subiculum pyramidal cell population may actually be comprised of discrete subclasses. Here, we investigated the extent and organizational principles governing pyramidal cell heterogeneity throughout the mouse subiculum. Using single-cell RNA-seq, we find that the subiculum pyramidal cell population can be deconstructed into eight separable subclasses. These subclasses were mapped onto abutting spatial domains, ultimately producing a complex laminar and columnar organization with heterogeneity across classical dorsal-ventral, proximal-distal, and superficial-deep axes. We further show that these transcriptomically defined subclasses correspond to differential protein products and can be associated with specific projection targets. This work deconstructs the complex landscape of subiculum pyramidal cells into spatially segregated subclasses that may be observed, controlled, and interpreted in future experiments.

Species and cell-type properties of classically defined human and rodent neurons and glia

Determination of the molecular properties of genetically targeted cell types has led to fundamental insights into mouse brain function and dysfunction. Here, we report an efficient strategy for precise exploration of gene expression and epigenetic events in specific cell types in a range of species, including postmortem human brain. We demonstrate that classically defined, homologous neuronal and glial cell types differ between rodent and human by the expression of hundreds of orthologous, cell specific genes. Confirmation that these genes are differentially active was obtained using epigenetic mapping and immunofluorescence localization. Studies of sixteen human postmortem brains revealed gender specific transcriptional differences, cell-specific molecular responses to aging, and the induction of a shared, robust response to an unknown external event evident in three donor samples. Our data establish a comprehensive approach for analysis of molecular events associated with specific circuits and cell types in a wide variety of human conditions.

Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing

Probing an individual cell's gene expression enables the identification of cell type and cell state. Single-cell RNA sequencing has emerged as a powerful tool for studying transcriptional profiles of cells, particularly in heterogeneous tissues such as the central nervous system. However, dissociation methods required for single cell sequencing can lead to experimental changes in the gene expression and cell death. Furthermore, these methods are generally restricted to fresh tissue, thus limiting studies on archival and bio-bank material. Single nucleus RNA sequencing (snRNA-Seq) is an appealing alternative for transcriptional studies, given that it accurately identifies cell types, permits the study of tissue that is frozen or difficult to dissociate, and reduces dissociation-induced transcription. Here, we present a high-throughput protocol for rapid isolation of nuclei for downstream snRNA-Seq. This method enables isolation of nuclei from fresh or frozen spinal cord samples and can be combined with two massively parallel droplet encapsulation platforms.

Integration of gene expression and brain-wide connectivity reveals the multiscale organization of mouse hippocampal networks

Understanding the organization of the hippocampus is fundamental to understanding brain function related to learning, memory, emotions, and diseases like Alzheimer’s disease. Physiological studies in humans and rodents suggest both structural and functional heterogeneity along the longitudinal axis of the hippocampus. Yet the recent discovery of discrete gene expression domains within the mouse hippocampus has provided the opportunity to re-evaluate hippocampal connectivity. To integrate mouse hippocampal gene expression and connectivity, we mapped the distribution of distinct gene expression patterns within mouse hippocampus and subiculum to create the Hippocampus Gene Expression Atlas (HGEA). Notably, novel subiculum gene expression patterns revealed a hidden laminar organization. Guided by the HGEA, we constructed the most detailed hippocampal connectome available using Mouse Connectome Project (www.MouseConnectome.org) tract tracing data. Our results define the hippocampus’ multiscale network organization and demonstrate each subnetwork’s unique brain-wide connectivity patterns.

The Use of Stem Cell-Derived Neurons for Understanding Development and Disease of the Cerebellum

The cerebellum is a fascinating brain structure, containing more neurons than the rest of the brain combined. The cerebellum develops according to a highly orchestrated program into a well-organized laminar structure. Much has been learned about the underlying genetic networks controlling cerebellar development through the study of various animal models. Cerebellar development in humans however, is significantly protracted and more complex. Given that the cerebellum regulates a number of motor and non-motor functions and is affected in a wide variety of neurodevelopmental and neurodegenerative disorders, a better understanding of human cerebellar development is highly desirable. Pluripotent stem cells offer an exciting new tool to unravel human cerebellar development and disease by providing a dynamic and malleable platform, which is amenable to genetic manipulation and temporally unrestricted sampling. It remains to be seen, however, whether in vitro neuronal cultures derived from pluripotent stem cells fully recapitulate the formation and organization of the developing nervous system, with many reports detailing the functionally immature nature of these cultures. Nevertheless, recent advances in differentiation protocols, cell-sampling methodologies, and access to informatics resources mean that the field is poised for remarkable discoveries. In this review, we provide a general overview of the field of neuronal differentiation, focusing on the cerebellum and highlighting conceptual advances in understanding neuronal maturity, including a discussion of both current and emerging methods to classify, and influence neuroanatomical identity and maturation status.

Single cell molecular alterations reveal target cells and pathways of concussive brain injury

The complex neuropathology of traumatic brain injury (TBI) is difficult to dissect, given the convoluted cytoarchitecture of affected brain regions such as the hippocampus. Hippocampal dysfunction during TBI results in cognitive decline that may escalate to other neurological disorders, the molecular basis of which is hidden in the genomic programs of individual cells. Using the unbiased single cell sequencing method Drop-seq, we report that concussive TBI affects previously undefined cell populations, in addition to classical hippocampal cell types. TBI also impacts cell type-specific genes and pathways and alters gene co-expression across cell types, suggesting hidden pathogenic mechanisms and therapeutic target pathways. Modulating the thyroid hormone pathway as informed by the T4 transporter transthyretin Ttr mitigates TBI-associated genomic and behavioral abnormalities. Thus, single cell genomics provides unique information about how TBI impacts diverse hippocampal cell types, adding new insights into the pathogenic pathways amenable to therapeutics in TBI and related disorders.

Experimental Considerations for Single-Cell RNA Sequencing Approaches

Single-cell transcriptomic technologies have emerged as powerful tools to explore cellular heterogeneity at the resolution of individual cells. Previous scientific knowledge in cell biology is largely limited to data generated by bulk profiling methods, which only provide averaged read-outs that generally mask cellular heterogeneity. This averaged approach is particularly problematic when the biological effect of interest is limited to only a subpopulation of cells such as stem/progenitor cells within a given tissue, or immune cell subsets infiltrating a tumor. Great advances in single-cell RNA sequencing (scRNAseq) enabled scientists to overcome this limitation and allow for in depth interrogation of previously unexplored rare cell types. Due to the high sensitivity of scRNAseq, adequate attention must be put into experimental setup and execution. Careful handling and processing of cells for scRNAseq is critical to preserve the native expression profile that will ensure meaningful analysis and conclusions. Here, we delineate the individual steps of a typical single-cell analysis workflow from tissue procurement, cell preparation, to platform selection and data analysis, and we discuss critical challenges in each of these steps, which will serve as a helpful guide to navigate the complex field of single-cell sequencing.

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.

Defining Cell Identity with Single-Cell Omics

Cells are a fundamental unit of life, and the ability to study the phenotypes and behaviors of individual cells is crucial to understanding the workings of complex biological systems. Cell phenotypes (epigenomic, transcriptomic, proteomic, and metabolomic) exhibit dramatic heterogeneity between and within the different cell types and states underlying cellular functional diversity. Cell genotypes can also display heterogeneity throughout an organism, in the form of somatic genetic variation-most notably in the emergence and evolution of tumors. Recent technical advances in single-cell isolation and the development of omics approaches sensitive enough to reveal these aspects of cell identity have enabled a revolution in the study of multicellular systems. In this review, we discuss the technologies available to resolve the genomes, epigenomes, transcriptomes, proteomes, and metabolomes of single cells from a wide variety of living systems.

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.

Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain

The mammalian brain is composed of diverse, specialized cell populations. To systematically ascertain and learn from these cellular specializations, we used Drop-seq to profile RNA expression in 690,000 individual cells sampled from 9 regions of the adult mouse brain. We identified 565 transcriptionally distinct groups of cells using computational approaches developed to distinguish biological from technical signals. Cross-region analysis of these 565 cell populations revealed features of brain organization, including a gene-expression module for synthesizing axonal and presynaptic components, patterns in the co-deployment of voltage-gated ion channels, functional distinctions among the cells of the vasculature and specialization of glutamatergic neurons across cortical regions. Systematic neuronal classifications for two complex basal ganglia nuclei and the striatum revealed a rare population of spiny projection neurons. This adult mouse brain cell atlas, accessible through interactive online software (DropViz), serves as a reference for development, disease, and evolution.

Single-cell RNA sequencing technologies and bioinformatics pipelines

Rapid progress in the development of next-generation sequencing (NGS) technologies in recent years has provided many valuable insights into complex biological systems, ranging from cancer genomics to diverse microbial communities. NGS-based technologies for genomics, transcriptomics, and epigenomics are now increasingly focused on the characterization of individual cells. These single-cell analyses will allow researchers to uncover new and potentially unexpected biological discoveries relative to traditional profiling methods that assess bulk populations. Single-cell RNA sequencing (scRNA-seq), for example, can reveal complex and rare cell populations, uncover regulatory relationships between genes, and track the trajectories of distinct cell lineages in development. In this review, we will focus on technical challenges in single-cell isolation and library preparation and on computational analysis pipelines available for analyzing scRNA-seq data. Further technical improvements at the level of molecular and cell biology and in available bioinformatics tools will greatly facilitate both the basic science and medical applications of these sequencing technologies.

Showing which genes are expressed, or switched on, in individual cells may help to reveal the first signs of disease. Each cell in an organism contains the same genetic information, but cell type and behavior depend on which genes are expressed. Previously, researchers could only sequence cells in batches, averaging the results, but technological improvements now allow sequencing of the genes expressed in an individual cell, known as single-cell RNA sequencing (scRNA-seq). Ji Hyun Lee (Kyung Hee University, Seoul) and Duhee Bang and Byungjin Hwang (Yonsei University, Seoul) have reviewed the available scRNA-seq technologies and the strategies available to analyze the large quantities of data produced. They conclude that scRNA-seq will impact both basic and medical science, from illuminating drug resistance in cancer to revealing the complex pathways of cell differentiation during development.

Centrosome aberrations and chromosome instability contribute to tumorigenesis and intra-tumor heterogeneity

Centrosomes serve as the major microtubule organizing centers in cells and thereby contribute to cell shape, polarity, and motility. Also, centrosomes ensure equal chromosome segregation during mitosis. Centrosome aberrations arise when the centrosome cycle is deregulated, or as a result of cytokinesis failure. A long-standing postulate is that centrosome aberrations are involved in the initiation and progression of cancer. However, this notion has been a subject of controversy because until recently the relationship has been correlative. Recently, it was shown that numerical or structural centrosome aberrations can initiate tumors in certain tissues in mice, as well as invasion. Particularly, we will focus on centrosome amplification and chromosome instability as drivers of intra-tumor heterogeneity and their consequences in cancer. We will also discuss briefly the controversies surrounding this theory to highlight the fact that the role of both centrosome amplification and chromosome instability in cancer is highly context-dependent. Further, we will discuss single-cell sequencing as a novel technique to understand intra-tumor heterogeneity and some therapeutic approaches to target chromosome instability.

Cell-specific histone modification maps in the human frontal lobe link schizophrenia risk to the neuronal epigenome

Risk variants for schizophrenia affect more than 100 genomic loci, yet cell- and tissue-specific roles underlying disease liability remain poorly characterized. We have generated for two cortical areas implicated in psychosis, the dorsolateral prefrontal cortex and anterior cingulate cortex, 157 reference maps from neuronal, neuron-depleted and bulk tissue chromatin for two histone marks associated with active promoters and enhancers, H3-trimethyl-Lys4 (H3K4me3) and H3-acetyl-Lys27 (H3K27ac). Differences between neuronal and neuron-depleted chromatin states were the major axis of variation in histone modification profiles, followed by substantial variability across subjects and cortical areas. Thousands of significant histone quantitative trait loci were identified in neuronal and neuron-depleted samples. Risk variants for schizophrenia, depressive symptoms and neuroticism were significantly over-represented in neuronal H3K4me3 and H3K27ac landscapes. Our Resource, sponsored by PsychENCODE and CommonMind, highlights the critical role of cell-type-specific signatures at regulatory and disease-associated noncoding sequences in the human frontal lobe.

DNA methylation analysis on purified neurons and glia dissects age and Alzheimer's disease-specific changes in the human cortex

Background

Epigenome-wide association studies (EWAS) based on human brain samples allow a deep and direct understanding of epigenetic dysregulation in Alzheimer’s disease (AD). However, strong variation of cell-type proportions across brain tissue samples represents a significant source of data noise. Here, we report the first EWAS based on sorted neuronal and non-neuronal (mostly glia) nuclei from postmortem human brain tissues.

Results

We show that cell sorting strongly enhances the robust detection of disease-related DNA methylation changes even in a relatively small cohort. We identify numerous genes with cell-type-specific methylation signatures and document differential methylation dynamics associated with aging specifically in neurons such as CLU, SYNJ2 and NCOR2 or in glia RAI1,CXXC5 and INPP5A. Further, we found neuron or glia-specific associations with AD Braak stage progression at genes such as MCF2L, ANK1, MAP2, LRRC8B, STK32C and S100B. A comparison of our study with previous tissue-based EWAS validates multiple AD-associated DNA methylation signals and additionally specifies their origin to neuron, e.g., HOXA3 or glia (ANK1). In a meta-analysis, we reveal two novel previously unrecognized methylation changes at the key AD risk genes APP and ADAM17.

Conclusions

Our data highlight the complex interplay between disease, age and cell-type-specific methylation changes in AD risk genes thus offering new perspectives for the validation and interpretation of large EWAS results.

Electronic supplementary material

The online version of this article (10.1186/s13072-018-0211-3) contains supplementary material, which is available to authorized users.

From Tissues to Cell Types and Back: Single-Cell Gene Expression Analysis of Tissue Architecture

With the recent transformative developments in single-cell genomics and, in particular, single-cell gene expression analysis, it is now possible to study tissues at the single-cell level, rather than having to rely on data from bulk measurements. Here we review the rapid developments in single-cell RNA sequencing (scRNA-seq) protocols that have the potential for unbiased identification and profiling of all cell types within a tissue or organism. In addition, novel approaches for spatial profiling of gene expression allow us to map individual cells and cell types back into the three-dimensional context of organs. The combination of in-depth single-cell and spatial gene expression data will reveal tissue architecture in unprecedented detail, generating a wealth of biological knowledge and a better understanding of many diseases.

The Human Cell Atlas: Technical approaches and challenges

The Human Cell Atlas is a large, international consortium that aims to identify and describe every cell type in the human body. The comprehensive cellular maps that arise from this ambitious effort have the potential to transform many aspects of fundamental biology and clinical practice. Here, we discuss the technical approaches that could be used today to generate such a resource and also the technical challenges that will be encountered.

Quantitative single-cell transcriptomics

Single-cell RNA sequencing (scRNA-seq) is currently transforming our understanding of biology, as it is a powerful tool to resolve cellular heterogeneity and molecular networks. Over 50 protocols have been developed in recent years and also data processing and analyzes tools are evolving fast. Here, we review the basic principles underlying the different experimental protocols and how to benchmark them. We also review and compare the essential methods to process scRNA-seq data from mapping, filtering, normalization and batch corrections to basic differential expression analysis. We hope that this helps to choose appropriate experimental and computational methods for the research question at hand.

Heterogeneity of Cell Surface Glutamate and GABA Receptor Expression in Shank and CNTN4 Autism Mouse Models

Autism spectrum disorder (ASD) refers to a large set of neurodevelopmental disorders, which have in common both repetitive behavior and abnormalities in social interactions and communication. Interestingly, most forms of ASD have a strong genetic contribution. However, the molecular underpinnings of this disorder remain elusive. The SHANK3 gene (and to a lesser degree SHANK2) which encode for the postsynaptic density (PSD) proteins SHANK3/SHANK2 and the CONTACTIN 4 gene which encodes for the neuronal glycoprotein CONTACTIN4 (CNTN4) exhibit mutated variants which are associated with ASD. Like many of the other genes associated with ASD, both SHANKs and CNTN4 affect synapse formation and function and are therefore related to the proper development and signaling capability of excitatory and inhibitory neuronal networks in the adult mammal brain. In this study, we used mutant/knock-out mice of Shank2 (Shank2^−/−), Shank3 (Shank3αβ^−/−), and Cntn4 (Cntn4^−/−) as ASD-models to explore whether these mice share a molecular signature in glutamatergic and GABAergic synaptic transmission in ASD-related brain regions. Using a biotinylation assay and subsequent western blotting we focused our analysis on cell surface expression of several ionotropic glutamate and GABA receptor subunits: GluA1, GluA2, and GluN1 were analyzed for excitatory synaptic transmission, and the α1 subunit of the GABA_A receptor was analyzed for inhibitory synaptic transmission. We found that both Shank2^−/− and Shank3αβ^−/− mice exhibit reduced levels of several cell surface glutamate receptors in the analyzed brain regions—especially in the striatum and thalamus—when compared to wildtype controls. Interestingly, even though Cntn4^−/− mice also show reduced levels of some cell surface glutamate receptors in the cortex and hippocampus, increased levels of cell surface glutamate receptors were found in the striatum. Moreover, Cntn4^−/− mice do not only show brain region-specific alterations in cell surface glutamate receptors but also a downregulation of cell surface GABA receptors in several of the analyzed brain regions. The results of this study suggest that even though mutations in defined genes can be associated with ASD this does not necessarily result in a common molecular phenotype in surface expression of glutamatergic and GABAergic receptor subunits in defined brain regions.

The State of the NIH BRAIN Initiative

The BRAIN Initiative arose from a grand challenge to "accelerate the development and application of new technologies that will enable researchers to produce dynamic pictures of the brain that show how individual brain cells and complex neural circuits interact at the speed of thought." The BRAIN Initiative is a public-private effort focused on the development and use of powerful tools for acquiring fundamental insights about how information processing occurs in the central nervous system (CNS). As the Initiative enters its fifth year, NIH has supported >500 principal investigators, who have answered the Initiative's challenge via hundreds of publications describing novel tools, methods, and discoveries that address the Initiative's seven scientific priorities. We describe scientific advances produced by individual laboratories, multi-investigator teams, and entire consortia that, over the coming decades, will produce more comprehensive and dynamic maps of the brain, deepen our understanding of how circuit activity can produce a rich tapestry of behaviors, and lay the foundation for understanding how its circuitry is disrupted in brain disorders. Much more work remains to bring this vision to fruition, and the National Institutes of Health continues to look to the diverse scientific community, from mathematics, to physics, chemistry, engineering, neuroethics, and neuroscience, to ensure that the greatest scientific benefit arises from this unique research Initiative.

New transcriptomic tools to understand testis development and functions

The testis plays a central role in the male reproductive system - secreting several hormones including male steroids and producing male gametes. A complex and coordinated molecular program is required for the proper differentiation of testicular cell types and maintenance of their functions in adulthood. The testicular transcriptome displays the highest levels of complexity and specificity across all tissues in a wide range of species. Many studies have used high-throughput sequencing technologies to define the molecular dynamics and regulatory networks in the testis as well as to identify novel genes or gene isoforms expressed in this organ. This review intends to highlight the complementarity of these transcriptomic studies and to show how the use of different sequencing protocols contribute to improve our global understanding of testicular biology.

SINC-seq: correlation of transient gene expressions between nucleus and cytoplasm reflects single-cell physiology

We report a microfluidic system that physically separates nuclear RNA (nucRNA) and cytoplasmic RNA (cytRNA) from a single cell and enables single-cell integrated nucRNA and cytRNA-sequencing (SINC-seq). SINC-seq constructs two individual RNA-seq libraries, nucRNA and cytRNA, per cell, quantifies gene expression in the subcellular compartments, and combines them to create novel single-cell RNA-seq data. Leveraging SINC-seq, we discover distinct natures of correlation among cytRNA and nucRNA that reflect the transient physiological state of single cells. These data provide unique insights into the regulatory network of messenger RNA from the nucleus toward the cytoplasm at the single-cell level.

Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics

Understanding any brain circuit will require a categorization of its constituent neurons. In hippocampal area CA1, at least 23 classes of GABAergic neuron have been proposed to date. However, this list may be incomplete; additionally, it is unclear whether discrete classes are sufficient to describe the diversity of cortical inhibitory neurons or whether continuous modes of variability are also required. We studied the transcriptomes of 3,663 CA1 inhibitory cells, revealing 10 major GABAergic groups that divided into 49 fine-scale clusters. All previously described and several novel cell classes were identified, with three previously described classes unexpectedly found to be identical. A division into discrete classes, however, was not sufficient to describe the diversity of these cells, as continuous variation also occurred between and within classes. Latent factor analysis revealed that a single continuous variable could predict the expression levels of several genes, which correlated similarly with it across multiple cell types. Analysis of the genes correlating with this variable suggested it reflects a range from metabolically highly active faster-spiking cells that proximally target pyramidal cells to slower-spiking cells targeting distal dendrites or interneurons. These results elucidate the complexity of inhibitory neurons in one of the simplest cortical structures and show that characterizing these cells requires continuous modes of variation as well as discrete cell classes.

Genetic identification of brain cell types underlying schizophrenia

With few exceptions, the marked advances in knowledge about the genetic basis of schizophrenia have not converged on findings that can be confidently used for precise experimental modeling. By applying knowledge of the cellular taxonomy of the brain from single-cell RNA sequencing, we evaluated whether the genomic loci implicated in schizophrenia map onto specific brain cell types. We found that the common-variant genomic results consistently mapped to pyramidal cells, medium spiny neurons (MSNs) and certain interneurons, but far less consistently to embryonic, progenitor or glial cells. These enrichments were due to sets of genes that were specifically expressed in each of these cell types. We also found that many of the diverse gene sets previously associated with schizophrenia (genes involved in synaptic function, those encoding mRNAs that interact with FMRP, antipsychotic targets, etc.) generally implicated the same brain cell types. Our results suggest a parsimonious explanation: the common-variant genetic results for schizophrenia point at a limited set of neurons, and the gene sets point to the same cells. The genetic risk associated with MSNs did not overlap with that of glutamatergic pyramidal cells and interneurons, suggesting that different cell types have biologically distinct roles in schizophrenia.