Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis

Pheno-seq - linking visual features and gene expression in 3D cell culture systems

Patient-derived 3D cell culture systems are currently advancing cancer research since they potentiate the molecular analysis of tissue-like properties and drug response under well-defined conditions. However, our understanding of the relationship between the heterogeneity of morphological phenotypes and the underlying transcriptome is still limited. To address this issue, we here introduce "pheno-seq" to directly link visual features of 3D cell culture systems with profiling their transcriptome. As prototypic applications breast and colorectal cancer (CRC) spheroids were analyzed by pheno-seq. We identified characteristic gene expression signatures of epithelial-to-mesenchymal transition that are associated with invasive growth behavior of clonal breast cancer spheroids. Furthermore, we linked long-term proliferative capacity in a patient-derived model of CRC to a lowly abundant PROX1-positive cancer stem cell subtype. We anticipate that the ability to integrate transcriptome analysis and morphological patho-phenotypes of cancer cells will provide novel insight on the molecular origins of intratumor heterogeneity.

The new technologies of high-throughput single-cell RNA sequencing

A wealth of genome and transcriptome data obtained using new generation sequencing (NGS) technologies for whole organisms could not answer many questions in oncology, immunology, physiology, neurobiology, zoology and other fields of science and medicine. Since the cell is the basis for the living of all unicellular and multicellular organisms, it is necessary to study the biological processes at its level. This understanding gave impetus to the development of a new direction – the creation of technologies that allow working with individual cells (single-cell technology). The rapid development of not only instruments, but also various advanced protocols for working with single cells is due to the relevance of these studies in many fields of science and medicine. Studying the features of various stages of ontogenesis, identifying patterns of cell differentiation and subsequent tissue development, conducting genomic and transcriptome analyses in various areas of medicine (especially in demand in immunology and oncology), identifying cell types and states, patterns of biochemical and physiological processes using single cell technologies, allows the comprehensive research to be conducted at a new level. The first RNA-sequencing technologies of individual cell transcriptomes (scRNA-seq) captured no more than one hundred cells at a time, which was insufficient due to the detection of high cell heterogeneity, existence of the minor cell types (which were not detected by morphology) and complex regulatory pathways. The unique techniques for isolating, capturing and sequencing transcripts of tens of thousands of cells at a time are evolving now. However, new technologies have certain differences both at the sample preparation stage and during the bioinformatics analysis. In the paper we consider the most effective methods of multiple parallel scRNA-seq using the example of 10XGenomics, as well as the specifics of such an experiment, further bioinformatics analysis of the data, future outlook and applications of new high-performance technologies.

Beyond bulk: a review of single cell transcriptomics methodologies and applications

Single-cell RNA sequencing (scRNA-seq) is a promising approach to study the transcriptomes of individual cells in the brain and the central nervous system (CNS). This technology acts as a bridge between neuroscience, computational biology, and systems biology, enabling an unbiased and novel understanding of the cellular composition of the brain and CNS. Gene expression at the single cell resolution is often noisy, sparse, and high-dimensional, creating challenges for computational analysis of such data. In this review, we overview fundamental sample preparation and data analysis processes of scRNA-seq and provide a comparative perspective for analyzing and visualizing these data.

Challenges in unsupervised clustering of single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) allows researchers to collect large catalogues detailing the transcriptomes of individual cells. Unsupervised clustering is of central importance for the analysis of these data, as it is used to identify putative cell types. However, there are many challenges involved. We discuss why clustering is a challenging problem from a computational point of view and what aspects of the data make it challenging. We also consider the difficulties related to the biological interpretation and annotation of the identified clusters.

Regionally specified human pluripotent stem cell-derived astrocytes exhibit different molecular signatures and functional properties

Astrocytes display diverse morphologies in different regions of the central nervous system. Whether astrocyte diversity is attributable to developmental processes and bears functional consequences, especially in humans, is unknown. RNA-seq of human pluripotent stem cell-derived regional astrocytes revealed distinct transcript profiles, suggesting differential functional properties. This was confirmed by differential calcium signaling as well as effects on neurite growth and blood-brain barrier formation. Distinct transcriptional profiles and functional properties of human astrocytes generated from regionally specified neural progenitors under the same conditions strongly implicate the developmental impact on astrocyte diversity. These findings provide a rationale for renewed examination of regional astrocytes and their role in the pathogenesis of psychiatric and neurological disorders.

Single-Cell RNA Sequencing of Oligodendrocyte Lineage Cells from the Mouse Central Nervous System

Single-cell RNA sequencing has emerged as a powerful technique for the identification of distinct cell states/populations in complex tissues. We have recently used this technology to investigate heterogeneity of cells of the oligodendrocyte lineage in the mouse central nervous system. In this chapter, we describe methods to perform single-cell RNA sequencing on this glial cell lineage, and discuss experimental and computational approaches to explore the potential and to tackle hurdles associated with this technology.

Differential Pathway Analysis

Integrating prior knowledge of pathway-level information can enhance power and facilitate interpretation of gene expression data analyses. Here, we provide a practical demonstration of the value of gene set or pathway enrichment testing and extend such techniques to identify and characterize transcriptional subpopulations from single-cell RNA-sequencing data using pathway and gene set overdispersion analysis (PAGODA).

A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys

Defining cellular and molecular identities within the kidney is necessary to understand its organization and function in health and disease. Here we demonstrate a reproducible method with minimal artifacts for single-nucleus Droplet-based RNA sequencing (snDrop-Seq) that we use to resolve thirty distinct cell populations in human adult kidney. We define molecular transition states along more than ten nephron segments spanning two major kidney regions. We further delineate cell type-specific expression of genes associated with chronic kidney disease, diabetes and hypertension, providing insight into possible targeted therapies. This includes expression of a hypertension-associated mechano-sensory ion channel in mesangial cells, and identification of proximal tubule cell populations defined by pathogenic expression signatures. Our fully optimized, quality-controlled transcriptomic profiling pipeline constitutes a tool for the generation of healthy and diseased molecular atlases applicable to clinical samples.

Single-cell studies in solid tissues remain challenging and have benefited from the development of single-nuclei RNA sequencing strategies. Here Lake et al. apply single-nucleus RNA sequencing to human kidney tissues to provide a comprehensive molecular and cellular atlas of the human kidney, with potential implications for the understanding of kidney physiology and disease.

Concepts and limitations for learning developmental trajectories from single cell genomics

Single cell genomics has become a popular approach to uncover the cellular heterogeneity of progenitor and terminally differentiated cell types with great precision. This approach can also delineate lineage hierarchies and identify molecular programmes of cell-fate acquisition and segregation. Nowadays, tens of thousands of cells are routinely sequenced in single cell-based methods and even more are expected to be analysed in the future. However, interpretation of the resulting data is challenging and requires computational models at multiple levels of abstraction. In contrast to other applications of single cell sequencing, where clustering approaches dominate, developmental systems are generally modelled using continuous structures, trajectories and trees. These trajectory models carry the promise of elucidating mechanisms of development, disease and stimulation response at very high molecular resolution. However, their reliable analysis and biological interpretation requires an understanding of their underlying assumptions and limitations. Here, we review the basic concepts of such computational approaches and discuss the characteristics of developmental processes that can be learnt from trajectory models.

Spatiotemporal structure of cell fate decisions in murine neural crest

Neural crest cells are embryonic progenitors that generate numerous cell types in vertebrates. With single-cell analysis, we show that mouse trunk neural crest cells become biased toward neuronal lineages when they delaminate from the neural tube, whereas cranial neural crest cells acquire ectomesenchyme potential dependent on activation of the transcription factor Twist1. The choices that neural crest cells make to become sensory, glial, autonomic, or mesenchymal cells can be formalized as a series of sequential binary decisions. Each branch of the decision tree involves initial coactivation of bipotential properties followed by gradual shifts toward commitment. Competing fate programs are coactivated before cells acquire fate-specific phenotypic traits. Determination of a specific fate is achieved by increased synchronization of relevant programs and concurrent repression of competing fate programs.

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.

Application of single-cell sequencing in human cancer

Precision medicine is emerging as a cornerstone of future cancer care with the objective of providing targeted therapies based on the molecular phenotype of each individual patient. Traditional bulk-level molecular phenotyping of tumours leads to significant information loss, as the molecular profile represents an average phenotype over large numbers of cells, while cancer is a disease with inherent intra-tumour heterogeneity at the cellular level caused by several factors, including clonal evolution, tissue hierarchies, rare cells and dynamic cell states. Single-cell sequencing provides means to characterize heterogeneity in a large population of cells and opens up opportunity to determine key molecular properties that influence clinical outcomes, including prognosis and probability of treatment response. Single-cell sequencing methods are now reliable enough to be used in many research laboratories, and we are starting to see applications of these technologies for characterization of human primary cancer cells. In this review, we provide an overview of studies that have applied single-cell sequencing to characterize human cancers at the single-cell level, and we discuss some of the current challenges in the field.

Single Cell Transcriptomes Derived from Human Cervical and Uterine Tissue during Pregnancy

This work presents the workflow for generating single cell transcriptomes derived from primary human uterine and cervical tissue obtained during planned cesarean hysterectomies. In total, a catalogue of 310 single cell transcriptomes are obtained, cell types present in these biopsies are inferred, and specific genes defining each of the cellular types present in the tissue are identified. Further validation of the inferred cell identity is also demonstrated via meta-analysis of independent repositories in literature generated by bulk sequenced data of fluorescence-activated cell sorting sorted cells.

Tracing the origin of adult intestinal stem cells

Adult intestinal stem cells are located at the bottom of crypts of Lieberkühn, where they express markers such as LGR5^1,2 and fuel the constant replenishment of the intestinal epithelium¹. Although fetal LGR5-expressing cells can give rise to adult intestinal stem cells^3,4, it remains unclear whether this population in the patterned epithelium represents unique intestinal stem-cell precursors. Here we show, using unbiased quantitative lineage-tracing approaches, biophysical modelling and intestinal transplantation, that all cells of the mouse intestinal epithelium-irrespective of their location and pattern of LGR5 expression in the fetal gut tube-contribute actively to the adult intestinal stem cell pool. Using 3D imaging, we find that during fetal development the villus undergoes gross remodelling and fission. This brings epithelial cells from the non-proliferative villus into the proliferative intervillus region, which enables them to contribute to the adult stem-cell niche. Our results demonstrate that large-scale remodelling of the intestinal wall and cell-fate specification are closely linked. Moreover, these findings provide a direct link between the observed plasticity and cellular reprogramming of differentiating cells in adult tissues following damage^5-9, revealing that stem-cell identity is an induced rather than a hardwired property.

Differential RA responsiveness directs formation of functionally distinct spermatogonial populations at the initiation of spermatogenesis in the mouse

In the mammalian testis, sustained spermatogenesis relies on spermatogonial stem cells (SSCs); their progeny either remain as stem cells (self-renewal) or proliferate and differentiate to enter meiosis in response to retinoic acid (RA). Here, we sought to uncover elusive mechanisms regulating a key switch fundamental to spermatogonial fate: the capacity of spermatogonia to respond to RA. Using the developing mouse testis as a model, we found that spermatogonia and precursor prospermatogonia exhibit a heterogeneous capacity to respond to RA with at least two underlying causes. First, progenitor spermatogonia are prevented from responding to RA by catabolic activity of cytochrome P450 family 26 enzymes. Second, a smaller subset of undifferentiated spermatogonia enriched for SSCs exhibit catabolism-independent RA insensitivity. Moreover, for the first time, we observed that precursor prospermatogonia are heterogeneous and comprise subpopulations that exhibit the same differential RA responsiveness found in neonatal spermatogonia. We propose a novel model by which mammalian prospermatogonial and spermatogonial fates are regulated by their intrinsic capacity to respond (or not) to the differentiation signal provided by RA before, and concurrent with, the initiation of spermatogenesis.

Modeling Motor Neuron Resilience in ALS Using Stem Cells

Oculomotor neurons, which regulate eye movement, are resilient to degeneration in the lethal motor neuron disease amyotrophic lateral sclerosis (ALS). It would be highly advantageous if motor neuron resilience could be modeled in vitro. Toward this goal, we generated a high proportion of oculomotor neurons from mouse embryonic stem cells through temporal overexpression of PHOX2A in neuronal progenitors. We demonstrate, using electrophysiology, immunocytochemistry, and RNA sequencing, that in vitro-generated neurons are bona fide oculomotor neurons based on their cellular properties and similarity to their in vivo counterpart in rodent and man. We also show that in vitro-generated oculomotor neurons display a robust activation of survival-promoting Akt signaling and are more resilient to the ALS-like toxicity of kainic acid than spinal motor neurons. Thus, we can generate bona fide oculomotor neurons in vitro that display a resilience similar to that seen in vivo.

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Bona fide oculomotor neurons can be derived from stem cells by PHOX2A overexpression

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In vitro- and in vivo-generated oculomotor neurons are transcriptionally similar

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Stem cell-derived oculomotor neurons display a robust activation of Akt signaling

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In vitro-generated oculomotor neurons are relatively resilient to ALS-like toxicity

Single-Cell Transcriptomics Uncovers Glial Progenitor Diversity and Cell Fate Determinants during Development and Gliomagenesis

The identity and degree of heterogeneity of glial progenitors and their contributions to brain tumor malignancy remain elusive. By applying lineage-targeted single-cell transcriptomics, we uncover an unanticipated diversity of glial progenitor pools with unique molecular identities in developing brain. Our analysis identifies distinct transitional intermediate states and their divergent developmental trajectories in astroglial and oligodendroglial lineages. Moreover, intersectional analysis uncovers analogous intermediate progenitors during brain tumorigenesis, wherein oligodendrocyte-progenitor intermediates are abundant, hyper-proliferative, and progressively reprogrammed toward a stem-like state susceptible to further malignant transformation. Similar actively cycling intermediate progenitors are prominent components in human gliomas with distinct driver mutations. We further unveil lineage-driving networks underlying glial fate specification and identify Zfp36l1 as necessary for oligodendrocyte-astrocyte lineage transition and glioma growth. Together, our results resolve the dynamic repertoire of common and divergent glial progenitors during development and tumorigenesis and highlight Zfp36l1 as a molecular nexus for balancing glial cell-fate decision and controlling gliomagenesis.

Analysis of Single-Cell RNA-seq Data by Clustering Approaches

Background:

The recently developed single-cell RNA sequencing (scRNA-seq) has attracted a great amount of attention due to its capability to interrogate expression of individual cells, which is superior to traditional bulk cell sequencing that can only measure mean gene expression of a population of cells. scRNA-seq has been successfully applied in finding new cell subtypes. New computational challenges exist in the analysis of scRNA-seq data.

Objective:

We provide an overview of the features of different similarity calculation and clustering methods, in order to facilitate users to select methods that are suitable for their scRNA-seq. We would also like to show that feature selection methods are important to improve clustering performance.

Results:

We first described similarity measurement methods, followed by reviewing some new clustering methods, as well as their algorithmic details. This analysis revealed several new questions, including how to automatically estimate the number of clustering categories, how to discover novel subpopulation, and how to search for new marker genes by using feature selection methods.

Conclusion:

Without prior knowledge about the number of cell types, clustering or semisupervised learning methods are important tools for exploratory analysis of scRNA-seq data.</P>

Single-Cell RNA-Seq Technologies and Related Computational Data Analysis

Single-cell RNA sequencing (scRNA-seq) technologies allow the dissection of gene expression at single-cell resolution, which greatly revolutionizes transcriptomic studies. A number of scRNA-seq protocols have been developed, and these methods possess their unique features with distinct advantages and disadvantages. Due to technical limitations and biological factors, scRNA-seq data are noisier and more complex than bulk RNA-seq data. The high variability of scRNA-seq data raises computational challenges in data analysis. Although an increasing number of bioinformatics methods are proposed for analyzing and interpreting scRNA-seq data, novel algorithms are required to ensure the accuracy and reproducibility of results. In this review, we provide an overview of currently available single-cell isolation protocols and scRNA-seq technologies, and discuss the methods for diverse scRNA-seq data analyses including quality control, read mapping, gene expression quantification, batch effect correction, normalization, imputation, dimensionality reduction, feature selection, cell clustering, trajectory inference, differential expression calling, alternative splicing, allelic expression, and gene regulatory network reconstruction. Further, we outline the prospective development and applications of scRNA-seq technologies.

From Pioneer to Repressor: Bimodal foxd3 Activity Dynamically Remodels Neural Crest Regulatory Landscape In Vivo

The neural crest (NC) is a transient embryonic stem cell-like population characterized by its multipotency and broad developmental potential. Here, we perform NC-specific transcriptional and epigenomic profiling of foxd3-mutant cells in vivo to define the gene regulatory circuits controlling NC specification. Together with global binding analysis obtained by foxd3 biotin-ChIP and single cell profiles of foxd3-expressing premigratory NC, our analysis shows that, during early steps of NC formation, foxd3 acts globally as a pioneer factor to prime the onset of genes regulating NC specification and migration by re-arranging the chromatin landscape, opening cis-regulatory elements and reshuffling nucleosomes. Strikingly, foxd3 then gradually switches from an activator to its well-described role as a transcriptional repressor and potentially uses differential partners for each role. Taken together, these results demonstrate that foxd3 acts bimodally in the neural crest as a switch from “permissive” to “repressive” nucleosome and chromatin organization to maintain multipotency and define cell fates.

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FoxD3 primes neural crest specification by modulating distal enhancers

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FoxD3 represses a number of neural crest migration and differentiation genes

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In neural crest, FoxD3 acts to switch chromatin from “permissive” to “repressive”

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Distinctive gene regulatory mechanisms underlie the bimodal action of FoxD3

A radical switch in clonality reveals a stem cell niche in the epiphyseal growth plate

Longitudinal bone growth in children is sustained by growth plates, narrow discs of cartilage that provide a continuous supply of chondrocytes for endochondral ossification¹. However, it remains unknown how this supply is maintained throughout childhood growth. Chondroprogenitors in the resting zone are thought to be gradually consumed as they supply cells for longitudinal growth^1,2, but this model has never been proved. Here, using clonal genetic tracing with multicolour reporters and functional perturbations, we demonstrate that longitudinal growth during the fetal and neonatal periods involves depletion of chondroprogenitors, whereas later in life, coinciding with the formation of the secondary ossification centre, chondroprogenitors acquire the capacity for self-renewal, resulting in the formation of large, stable monoclonal columns of chondrocytes. Simultaneously, chondroprogenitors begin to express stem cell markers and undergo symmetric cell division. Regulation of the pool of self-renewing progenitors involves the hedgehog and mammalian target of rapamycin complex 1 (mTORC1) signalling pathways. Our findings indicate that a stem cell niche develops postnatally in the epiphyseal growth plate, which provides a continuous supply of chondrocytes over a prolonged period.

Single-Cell Transcriptomics and Fate Mapping of Ependymal Cells Reveals an Absence of Neural Stem Cell Function

Ependymal cells are multi-ciliated cells that form the brain's ventricular epithelium and a niche for neural stem cells (NSCs) in the ventricular-subventricular zone (V-SVZ). In addition, ependymal cells are suggested to be latent NSCs with a capacity to acquire neurogenic function. This remains highly controversial due to a lack of prospective in vivo labeling techniques that can effectively distinguish ependymal cells from neighboring V-SVZ NSCs. We describe a transgenic system that allows for targeted labeling of ependymal cells within the V-SVZ. Single-cell RNA-seq revealed that ependymal cells are enriched for cilia-related genes and share several stem-cell-associated genes with neural stem or progenitors. Under in vivo and in vitro neural-stem- or progenitor-stimulating environments, ependymal cells failed to demonstrate any suggestion of latent neural-stem-cell function. These findings suggest remarkable stability of ependymal cell function and provide fundamental insights into the molecular signature of the V-SVZ niche.

Disease Pathway Cut for Multi-Target drugs

Background

Biomarker discovery studies have been moving the focus from a single target gene to a set of target genes. However, the number of target genes in a drug should be minimum to avoid drug side-effect or toxicity. But still, the set of target genes should effectively block all possible paths of disease progression.

Methods

In this article, we propose a network based computational analysis for target gene identification for multi-target drugs. The min-cut algorithm is employed to cut all the paths from onset genes to apoptotic genes on a disease pathway. If the pathway network is completely disconnected, development of disease will not further go on. The genes corresponding to the end points of the cutting edges are identified as candidate target genes for a multi-target drug.

Results and conclusions

The proposed method was applied to 10 disease pathways. In total, thirty candidate genes were suggested. The result was validated with gene set enrichment analysis software, PubMed literature review and de facto drug targets.

Single-Cell Analysis of Diverse Pathogen Responses Defines a Molecular Roadmap for Generating Antigen-Specific Immunity

The immune system generates pathogen-tailored responses. The precise innate immune cell types and pathways that direct robust adaptive immune responses have not been fully characterized. By using fluorescent pathogens combined with massively parallel single-cell RNA-seq, we comprehensively characterized the initial 48 h of the innate immune response to diverse pathogens. We found that across all pathogens tested, most of the lymph node cell types and states showed little pathogen specificity. In contrast, the rare antigen-positive cells displayed pathogen-specific transcriptional programs as early as 24 h after immunization. In addition, mycobacteria activated a specific NK-driven IFNγ response. Depletion of NK cells and IFNγ showed that IFNγ initiated a monocyte-specific signaling cascade, leading to the production of major chemokines and cytokines that promote Th1 development. Our systems immunology approach sheds light on early events in innate immune responses and may help further development of safe and efficient vaccines.

Single-cell analysis reveals fibroblast heterogeneity and myeloid-derived adipocyte progenitors in murine skin wounds

During wound healing in adult mouse skin, hair follicles and then adipocytes regenerate. Adipocytes regenerate from myofibroblasts, a specialized contractile wound fibroblast. Here we study wound fibroblast diversity using single-cell RNA-sequencing. On analysis, wound fibroblasts group into twelve clusters. Pseudotime and RNA velocity analyses reveal that some clusters likely represent consecutive differentiation states toward a contractile phenotype, while others appear to represent distinct fibroblast lineages. One subset of fibroblasts expresses hematopoietic markers, suggesting their myeloid origin. We validate this finding using single-cell western blot and single-cell RNA-sequencing on genetically labeled myofibroblasts. Using bone marrow transplantation and Cre recombinase-based lineage tracing experiments, we rule out cell fusion events and confirm that hematopoietic lineage cells give rise to a subset of myofibroblasts and rare regenerated adipocytes. In conclusion, our study reveals that wounding induces a high degree of heterogeneity among fibroblasts and recruits highly plastic myeloid cells that contribute to adipocyte regeneration.

The diversity of fibroblasts contributing to wound healing is unclear. Here, the authors use single-cell RNA-sequencing to identify heterogeneity among murine fibroblasts in the wound and find that recruited myeloid cells contribute to adipocyte regeneration during healing.

Revolutionizing Cancer Immunology: The Power of Next-Generation Sequencing Technologies

It has long been appreciated that tumors are diverse, varying in mutational status, composition of cellular infiltrate, and organizational architecture. For the most part, the information embedded in this diversity has gone untapped due to the limited resolution and dimensionality of assays for analyzing nucleic acid expression in cells. The advent of high-throughput, next-generation sequencing (NGS) technologies that measure nucleic acids, particularly at the single-cell level, is fueling the characterization of the many components that comprise the tumor microenvironment (TME), with a strong focus on immune composition. Understanding the immune and nonimmune components of the TME, how they interact, and how this shapes their functional properties requires the development of novel computational methods and, eventually, the application of systems-based approaches. The continued development and application of NGS technologies holds great promise for accelerating discovery in the cancer immunology field.

Differentiation and maturation of oligodendrocytes in human three-dimensional neural cultures

Investigating human oligodendrogenesis and the interaction of oligodendrocytes with neurons and astrocytes would accelerate our understanding of the mechanisms underlying white matter disorders. However, this is challenging due to limited accessibility of functional human brain tissue. Here, we developed a novel differentiation method of human induced pluripotent stem cells (hiPS cells) to generate three-dimensional (3D) neural spheroids that contain oligodendrocytes as well as neurons and astrocytes, called human oligodendrocyte spheroids (hOLS). We demonstrate that oligodendrocyte-lineage cells derived in hOLS transition through developmental stages similar to primary human oligodendrocytes and that the migration of oligodendrocyte-lineage cells and their susceptibility to lysolecithin exposure can be captured by live imaging. Moreover, their morphology changes as they mature over time in vitro and start myelinating neurons. We anticipate that this method can be used to study oligodendrocyte development, myelination, and interactions with other major cell types in the central nervous system.

Gene Regulatory Networks from Single Cell Data for Exploring Cell Fate Decisions

Single cell experimental techniques now allow us to quantify gene expression in up to thousands of individual cells. These data reveal the changes in transcriptional state that occur as cells progress through development and adopt specialized cell fates. In this chapter we describe in detail how to use our network inference algorithm (PIDC)-and the associated software package NetworkInference.jl-to infer functional interactions between genes from the observed gene expression patterns. We exploit the large sample sizes and inherent variability of single cell data to detect statistical dependencies between genes that indicate putative (co-)regulatory relationships, using multivariate information measures that can capture complex statistical relationships. We provide guidelines on how best to combine this analysis with other complementary methods designed to explore single cell data, and how to interpret the resulting gene regulatory network models to gain insight into the processes regulating cell differentiation.

An Informative Approach to Single-Cell Sequencing Analysis

Recent advances in sequencing technologies enable us to obtain genome, epigenome and transcriptome data in individual cells. In this review, we describe various platforms for single-cell sequencing analysis across multiple layers. We mainly introduce an automated single-cell RNA-seq platform, the Chromium Single Cell 3' RNA-seq system, and its technical features and compare it with other single-cell RNA-seq systems. We also describe computational methods for analyzing large, complex single-cell datasets. Due to the insufficient depth of single-cell RNA-seq data, resulting in a critical lack of transcriptome information for low-expressed genes, it is occasionally difficult to interpret the data as is. To overcome the analytical problems for such sparse datasets, there are many bioinformatics reports that provide informative approaches, including imputation, correction of batch effects, dimensional reduction and clustering.

Visualizing and Interpreting Single-Cell Gene Expression Datasets with Similarity Weighted Nonnegative Embedding

High-throughput single-cell gene expression profiling has enabled the definition of new cell types and developmental trajectories. Visualizing these datasets is crucial to biological interpretation, and a popular method is t-stochastic neighbor embedding (t-SNE), which visualizes local patterns well but distorts global structure, such as distances between clusters. We developed similarity weighted nonnegative embedding (SWNE), which enhances interpretation of datasets by embedding the genes and factors that separate cell states on the visualization alongside the cells and maintains fidelity when visualizing local and global structure for both developmental trajectories and discrete cell types. SWNE uses nonnegative matrix factorization to decompose the gene expression matrix into biologically relevant factors; embeds the cells, genes, and factors in a 2D visualization; and uses a similarity matrix to smooth the embeddings. We demonstrate SWNE on single-cell RNA-seq data from hematopoietic progenitors and human brain cells. SWNE is available as an R package at github.com/yanwu2014/swne.

Deep generative modeling for single-cell transcriptomics

Single-cell transcriptome measurements can reveal unexplored biological diversity, but they suffer from technical noise and bias that must be modeled to account for the resulting uncertainty in downstream analyses. Here we introduce single-cell variational inference (scVI), a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells ( https://github.com/YosefLab/scVI ). scVI uses stochastic optimization and deep neural networks to aggregate information across similar cells and genes and to approximate the distributions that underlie observed expression values, while accounting for batch effects and limited sensitivity. We used scVI for a range of fundamental analysis tasks including batch correction, visualization, clustering, and differential expression, and achieved high accuracy for each task.

Longitudinal single-cell RNA sequencing of patient-derived primary cells reveals drug-induced infidelity in stem cell hierarchy

Chemo-resistance is one of the major causes of cancer-related deaths. Here we used single-cell transcriptomics to investigate divergent modes of chemo-resistance in tumor cells. We observed that higher degree of phenotypic intra-tumor heterogeneity (ITH) favors selection of pre-existing drug-resistant cells, whereas phenotypically homogeneous cells engage covert epigenetic mechanisms to trans-differentiate under drug-selection. This adaptation was driven by selection-induced gain of H3K27ac marks on bivalently poised resistance-associated chromatin, and therefore not expressed in the treatment-naïve setting. Mechanistic interrogation of this phenomenon revealed that drug-induced adaptation was acquired upon the loss of stem factor SOX2, and a concomitant gain of SOX9. Strikingly we observed an enrichment of SOX9 at drug-induced H3K27ac sites, suggesting that tumor evolution could be driven by stem cell-switch-mediated epigenetic plasticity. Importantly, JQ1 mediated inhibition of BRD4 could reverse drug-induced adaptation. These results provide mechanistic insights into the modes of therapy-induced cellular plasticity and underscore the use of epigenetic inhibitors in targeting tumor evolution.

Drug resistance is one of the major causes of cancer-related deaths. Here, the authors using single cell RNA-seq of oral squamous cell carcinoma patient samples pre- and post-cisplatin treatment show that phenotypically homogenous cell populations display cell state plasticity, with poised chromatin marks at mesenchymal genes in epithelial cells, and that the loss of stem factor Sox2 but gain of Sox9 expression (with de novo gain of H3K27ac sites) is associated with drug-induced adaptation.

VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies

We develop a method, VIPER, to impute the zero values in single-cell RNA sequencing studies to facilitate accurate transcriptome quantification at the single-cell level. VIPER is based on nonnegative sparse regression models and is capable of progressively inferring a sparse set of local neighborhood cells that are most predictive of the expression levels of the cell of interest for imputation. A key feature of our method is its ability to preserve gene expression variability across cells after imputation. We illustrate the advantages of our method through several well-designed real data-based analytical experiments.

T-ALL leukemia stem cell 'stemness' is epigenetically controlled by the master regulator SPI1

Leukemia stem cells (LSCs) are regarded as the origins and key therapeutic targets of leukemia, but limited knowledge is available on the key determinants of LSC 'stemness'. Using single-cell RNA-seq analysis, we identify a master regulator, SPI1, the LSC-specific expression of which determines the molecular signature and activity of LSCs in the murine Pten-null T-ALL model. Although initiated by PTEN-controlled β-catenin activation, Spi1 expression and LSC 'stemness' are maintained by a β-catenin-SPI1-HAVCR2 regulatory circuit independent of the leukemogenic driver mutation. Perturbing any component of this circuit either genetically or pharmacologically can prevent LSC formation or eliminate existing LSCs. LSCs lose their 'stemness' when Spi1 expression is silenced by DNA methylation, but Spi1 expression can be reactivated by 5-AZ treatment. Importantly, similar regulatory mechanisms may be also present in human T-ALL.

A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade

Immune checkpoint inhibitors (ICIs) produce durable responses in some melanoma patients, but many patients derive no clinical benefit, and the molecular underpinnings of such resistance remain elusive. Here, we leveraged single-cell RNA sequencing (scRNA-seq) from 33 melanoma tumors and computational analyses to interrogate malignant cell states that promote immune evasion. We identified a resistance program expressed by malignant cells that is associated with T cell exclusion and immune evasion. The program is expressed prior to immunotherapy, characterizes cold niches in situ, and predicts clinical responses to anti-PD-1 therapy in an independent cohort of 112 melanoma patients. CDK4/6-inhibition represses this program in individual malignant cells, induces senescence, and reduces melanoma tumor outgrowth in mouse models in vivo when given in combination with immunotherapy. Our study provides a high-resolution landscape of ICI-resistant cell states, identifies clinically predictive signatures, and suggests new therapeutic strategies to overcome immunotherapy resistance.

Enter the Matrix: Factorization Uncovers Knowledge from Omics

Omics data contain signals from the molecular, physical, and kinetic inter- and intracellular interactions that control biological systems. Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis. We review exemplary applications of MF for systems-level analyses. We discuss appropriate applications of these methods, their limitations, and focus on the analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with MF enables discovery from high-throughput data beyond the limits of current biological knowledge - answering questions from high-dimensional data that we have not yet thought to ask.

Aberrant ERBB4-SRC Signaling as a Hallmark of Group 4 Medulloblastoma Revealed by Integrative Phosphoproteomic Profiling

The current consensus recognizes four main medulloblastoma subgroups (wingless, Sonic hedgehog, group 3 and group 4). While medulloblastoma subgroups have been characterized extensively at the (epi-)genomic and transcriptomic levels, the proteome and phosphoproteome landscape remain to be comprehensively elucidated. Using quantitative (phospho)-proteomics in primary human medulloblastomas, we unravel distinct posttranscriptional regulation leading to highly divergent oncogenic signaling and kinase activity profiles in groups 3 and 4 medulloblastomas. Specifically, proteomic and phosphoproteomic analyses identify aberrant ERBB4-SRC signaling in group 4. Hence, enforced expression of an activated SRC combined with p53 inactivation induces murine tumors that resemble group 4 medulloblastoma. Therefore, our integrative proteogenomics approach unveils an oncogenic pathway and potential therapeutic vulnerability in the most common medulloblastoma subgroup.

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

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

Comparative transcriptomic analyses and single-cell RNA sequencing of the freshwater planarian Schmidtea mediterranea identify major cell types and pathway conservation

BACKGROUND

In the Lophotrochozoa/Spiralia superphylum, few organisms have as high a capacity for rapid testing of gene function and single-cell transcriptomics as the freshwater planaria. The species Schmidtea mediterranea in particular has become a powerful model to use in studying adult stem cell biology and mechanisms of regeneration. Despite this, systematic attempts to define gene complements and their annotations are lacking, restricting comparative analyses that detail the conservation of biochemical pathways and identify lineage-specific innovations.

RESULTS

In this study we compare several transcriptomes and define a robust set of 35,232 transcripts. From this, we perform systematic functional annotations and undertake a genome-scale metabolic reconstruction for S. mediterranea. Cross-species comparisons of gene content identify conserved, lineage-specific, and expanded gene families, which may contribute to the regenerative properties of planarians. In particular, we find that the TRAF gene family has been greatly expanded in planarians. We further provide a single-cell RNA sequencing analysis of 2000 cells, revealing both known and novel cell types defined by unique signatures of gene expression. Among these are a novel mesenchymal cell population as well as a cell type involved in eye regeneration. Integration of our metabolic reconstruction further reveals the extent to which given cell types have adapted energy and nucleotide biosynthetic pathways to support their specialized roles.

CONCLUSIONS

In general, S. mediterranea displays a high level of gene and pathway conservation compared with other model systems, rendering it a viable model to study the roles of these pathways in stem cell biology and regeneration.

Transcriptional Convergence of Oligodendrocyte Lineage Progenitors during Development

Pdgfra+ oligodendrocyte precursor cells (OPCs) arise in distinct specification waves during embryogenesis in the central nervous system (CNS). It is unclear whether there is a correlation between these waves and different oligodendrocyte (OL) states at adult stages. Here, we present bulk and single-cell transcriptomics resources providing insights on how transitions between these states occur. We found that post-natal OPCs from brain and spinal cord present similar transcriptional signatures. Moreover, post-natal OPC progeny of E13.5 Pdgfra+ cells present electrophysiological and transcriptional profiles similar to OPCs derived from subsequent specification waves, indicating that Pdgfra+ pre-OPCs rewire their transcriptional network during development. Single-cell RNA-seq and lineage tracing indicates that a subset of E13.5 Pdgfra+ cells originates cells of the pericyte lineage. Thus, our results indicate that embryonic Pdgfra+ cells in the CNS give rise to distinct post-natal cell lineages, including OPCs with convergent transcriptional profiles in different CNS regions.

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OPCs arising from different parts of the CNS are highly similar to one another

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Pre-OPCs converge into similar transcriptional and electrophysiological OPC states

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E13.5 Pdgfra+ cells give rise mainly to OPCs in the post-natal CNS

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E13.5 Pdgfra+ cells also give rise to cells of the pericyte lineage

ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data

Motivation

Single-cell RNA-sequencing (scRNA-seq) allows whole transcriptome profiling of thousands of individual cells, enabling the molecular exploration of tissues at the cellular level. Such analytical capacity is of great interest to many research groups in the world, yet these groups often lack the expertise to handle complex scRNA-seq datasets.

Results

We developed a fully integrated, web-based platform aimed at the complete analysis of scRNA-seq data post genome alignment: from the parsing, filtering and normalization of the input count data files, to the visual representation of the data, identification of cell clusters, differentially expressed genes (including cluster-specific marker genes), and functional gene set enrichment. This Automated Single-cell Analysis Pipeline (ASAP) combines a wide range of commonly used algorithms with sophisticated visualization tools. Compared with existing scRNA-seq analysis platforms, researchers (including those lacking computational expertise) are able to interact with the data in a straightforward fashion and in real time. Furthermore, given the overlap between scRNA-seq and bulk RNA-seq analysis workflows, ASAP should conceptually be broadly applicable to any RNA-seq dataset. As a validation, we demonstrate how we can use ASAP to simply reproduce the results from a single-cell study of 91 mouse cells involving five distinct cell types.

Availability and implementation

The tool is freely available at asap.epfl.ch and R/Python scripts are available at github.com/DeplanckeLab/ASAP.

Supplementary information

Supplementary data are available at Bioinformatics online.

Toward Minimal Residual Disease-Directed Therapy in Melanoma

Many patients with advanced cancers achieve dramatic responses to a panoply of therapeutics yet retain minimal residual disease (MRD), which ultimately results in relapse. To gain insights into the biology of MRD, we applied single-cell RNA sequencing to malignant cells isolated from BRAF mutant patient-derived xenograft melanoma cohorts exposed to concurrent RAF/MEK-inhibition. We identified distinct drug-tolerant transcriptional states, varying combinations of which co-occurred within MRDs from PDXs and biopsies of patients on treatment. One of these exhibited a neural crest stem cell (NCSC) transcriptional program largely driven by the nuclear receptor RXRG. An RXR antagonist mitigated accumulation of NCSCs in MRD and delayed the development of resistance. These data identify NCSCs as key drivers of resistance and illustrate the therapeutic potential of MRD-directed therapy. They also highlight how gene regulatory network architecture reprogramming may be therapeutically exploited to limit cellular heterogeneity, a key driver of disease progression and therapy resistance.

The impact of single-cell RNA sequencing on understanding the functional organization of the immune system

Application of single-cell genomics technologies has revolutionized our approach to study the immune system. Unravelling the functional diversity of immune cells and their coordinated response is key to understanding immunity. Single-cell transcriptomics technologies provide high-dimensional assessment of the transcriptional states of immune cells and have been successfully applied to discover new immune cell types, reveal haematopoietic lineages, identify gene modules dictating immune responses and investigate lymphocyte antigen receptor diversity. In this review, we discuss the impact and applications of single-cell RNA sequencing technologies in immunology.

Mapping gene regulatory networks from single-cell omics data

Single-cell techniques are advancing rapidly and are yielding unprecedented insight into cellular heterogeneity. Mapping the gene regulatory networks (GRNs) underlying cell states provides attractive opportunities to mechanistically understand this heterogeneity. In this review, we discuss recently emerging methods to map GRNs from single-cell transcriptomics data, tackling the challenge of increased noise levels and data sparsity compared with bulk data, alongside increasing data volumes. Next, we discuss how new techniques for single-cell epigenomics, such as single-cell ATAC-seq and single-cell DNA methylation profiling, can be used to decipher gene regulatory programmes. We finally look forward to the application of single-cell multi-omics and perturbation techniques that will likely play important roles for GRN inference in the future.