Mapping chromatin modifications at the single cell level

Prediction of cell-type-specific cohesin-mediated chromatin loops based on chromatin state

Chromatin loop is of crucial importance for the regulation of gene transcription. Cohesin is a type of chromatin-associated protein that mediates the interaction of chromatin through the loop extrusion. Cohesin-mediated chromatin interactions have strong cell-type specificity, posing a challenge for predicting chromatin loops. Existing computational methods perform poorly in predicting cell-type-specific chromatin loops. To address this issue, we propose a random forest model to predict cell-type-specific cohesin-mediated chromatin loops based on chromatin states identified by ChromHMM and the occupancy of related factors. Our results show that chromatin state is responsible for cell-type-specificity of loops. Using only chromatin states as features, the model achieved high accuracy in predicting cell-type-specific loops between two cell types and can be applied to different cell types. Furthermore, when chromatin states are combined with the occurrence frequency of CTCF, RAD21, YY1, and H3K27ac ChIP-seq peaks, more accurate prediction can be achieved. Our feature extraction method provides novel insights into predicting cell-type-specific chromatin loops and reveals the relationship between chromatin state and chromatin loop formation.

Deep Batch Integration and Denoise of Single-Cell RNA-Seq Data

Numerous single-cell transcriptomic datasets from identical tissues or cell lines are generated from different laboratories or single-cell RNA sequencing (scRNA-seq) protocols. The denoising of these datasets to eliminate batch effects is crucial for data integration, ensuring accurate interpretation and comprehensive analysis of biological questions. Although many scRNA-seq data integration methods exist, most are inefficient and/or not conducive to downstream analysis. Here, DeepBID, a novel deep learning-based method for batch effect correction, non-linear dimensionality reduction, embedding, and cell clustering concurrently, is introduced. DeepBID utilizes a negative binomial-based autoencoder with dual Kullback-Leibler divergence loss functions, aligning cell points from different batches within a consistent low-dimensional latent space and progressively mitigating batch effects through iterative clustering. Extensive validation on multiple-batch scRNA-seq datasets demonstrates that DeepBID surpasses existing tools in removing batch effects and achieving superior clustering accuracy. When integrating multiple scRNA-seq datasets from patients with Alzheimer's disease, DeepBID significantly improves cell clustering, effectively annotating unidentified cells, and detecting cell-specific differentially expressed genes.

Chromatinopathies: insight in clinical aspects and underlying epigenetic changes

Chromatinopathies (CPs), a group of rare inborn defects characterized by chromatin state imbalance, have evolved from initially resembling Cornelia de Lange syndrome to encompass a wide array of genetic diseases with diverse clinical presentations. The CPs classification now includes human developmental disorders caused by germline mutations in epigenes, genes that regulate the epigenome. Recent advances in next-generation sequencing have enabled the association of 154 epigenes with CPs, revealing distinctive DNA methylation patterns known as episignatures.It has been shown that episignatures are unique for a particular CP or share similarities among specific CP subgroup. Consequently, these episignatures have emerged as promising biomarkers for diagnosing and treating CPs, differentiating subtypes, evaluating variants of unknown significance, and facilitating targeted therapies tailored to the underlying epigenetic dysregulation.The following review was conducted to collect, summarize, and analyze data regarding CPs in such aspects as clinical evaluation encompassing long-term patient care, underlying epigenetic changes, and innovative molecular and bioinformatic methodologies that have been devised for the assessment of CPs. We have also shed light on promising novel treatment options that have surfaced in recent research and presented a synthesis of ongoing clinical trials, contributing to the current understanding of the dynamic and evolving nature of CPs investigation.

The omics era: a nexus of untapped potential for Mendelian chromatinopathies

The OMICs cascade describes the hierarchical flow of information through biological systems. The epigenome sits at the apex of the cascade, thereby regulating the RNA and protein expression of the human genome and governs cellular identity and function. Genes that regulate the epigenome, termed epigenes, orchestrate complex biological signaling programs that drive human development. The broad expression patterns of epigenes during human development mean that pathogenic germline mutations in epigenes can lead to clinically significant multi-system malformations, developmental delay, intellectual disabilities, and stem cell dysfunction. In this review, we refer to germline developmental disorders caused by epigene mutation as "chromatinopathies". We curated the largest number of human chromatinopathies to date and our expanded approach more than doubled the number of established chromatinopathies to 179 disorders caused by 148 epigenes. Our study revealed that 20.6% (148/720) of epigenes cause at least one chromatinopathy. In this review, we highlight key examples in which OMICs approaches have been applied to chromatinopathy patient biospecimens to identify underlying disease pathogenesis. The rapidly evolving OMICs technologies that couple molecular biology with high-throughput sequencing or proteomics allow us to dissect out the causal mechanisms driving temporal-, cellular-, and tissue-specific expression. Using the full repertoire of data generated by the OMICs cascade to study chromatinopathies will provide invaluable insight into the developmental impact of these epigenes and point toward future precision targets for these rare disorders.

How single-cell techniques help us look into lung cancer heterogeneity and immunotherapy

Lung cancer patients tend to have strong intratumoral and intertumoral heterogeneity and complex tumor microenvironment, which are major contributors to the efficacy of and drug resistance to immunotherapy. From a new perspective, single-cell techniques offer an innovative way to look at the intricate cellular interactions between tumors and the immune system and help us gain insights into lung cancer and its response to immunotherapy. This article reviews the application of single-cell techniques in lung cancer, with focuses directed on the heterogeneity of lung cancer and the efficacy of immunotherapy. This review provides both theoretical and experimental information for the future development of immunotherapy and personalized treatment for the management of lung cancer.

Single-Cell Analysis in Immuno-Oncology

The complexity of the cellular and non-cellular milieu surrounding human tumors plays a decisive role in the course and outcome of disease. The high variability in the distribution of the immune and non-immune compartments within the tumor microenvironments (TME) among different patients governs the mode of their response or resistance to current immunotherapeutic approaches. Through deciphering this diversity, one can tailor patients' management to meet an individual's needs. Single-cell (sc) omics technologies have given a great boost towards this direction. This review gathers recent data about how multi-omics profiling, including the utilization of single-cell RNA sequencing (scRNA-seq), assay for transposase-accessible chromatin with sequencing (scATAC-seq), T-cell receptor sequencing (scTCR-seq), mass, tissue-based, or microfluidics cytometry, and related bioinformatics tools, contributes to the high-throughput assessment of a large number of analytes at single-cell resolution. Unravelling the exact TCR clonotype of the infiltrating T cells or pinpointing the classical or novel immune checkpoints across various cell subsets of the TME provide a boost to our comprehension of adaptive immune responses, their antigen specificity and dynamics, and grant suggestions for possible therapeutic targets. Future steps are expected to merge high-dimensional data with tissue localization data, which can serve the investigation of novel multi-modal biomarkers for the selection and/or monitoring of the optimal treatment from the current anti-cancer immunotherapeutic armamentarium.

Mammalian DNA methylome dynamics: mechanisms, functions and new frontiers

DNA methylation is a highly conserved epigenetic modification that plays essential roles in mammalian gene regulation, genome stability and development. Despite being primarily considered a stable and heritable epigenetic silencing mechanism at heterochromatic and repetitive regions, whole genome methylome analysis reveals that DNA methylation can be highly cell-type specific and dynamic within proximal and distal gene regulatory elements during early embryonic development, stem cell differentiation and reprogramming, and tissue maturation. In this Review, we focus on the mechanisms and functions of regulated DNA methylation and demethylation, highlighting how these dynamics, together with crosstalk between DNA methylation and histone modifications at distinct regulatory regions, contribute to mammalian development and tissue maturation. We also discuss how recent technological advances in single-cell and long-read methylome sequencing, along with targeted epigenome-editing, are enabling unprecedented high-resolution and mechanistic dissection of DNA methylome dynamics.

Epigenetic tumor heterogeneity in the era of single-cell profiling with nanopore sequencing

Nanopore sequencing has brought the technology to the next generation in the science of sequencing. This is achieved through research advancing on: pore efficiency, creating mechanisms to control DNA translocation, enhancing signal-to-noise ratio, and expanding to long-read ranges. Heterogeneity regarding epigenetics would be broad as mutations in the epigenome are sensitive to cause new challenges in cancer research. Epigenetic enzymes which catalyze DNA methylation and histone modification are dysregulated in cancer cells and cause numerous heterogeneous clones to evolve. Detection of this heterogeneity in these clones plays an indispensable role in the treatment of various cancer types. With single-cell profiling, the nanopore sequencing technology could provide a simple sequence at long reads and is expected to be used soon at the bedside or doctor's office. Here, we review the advancements of nanopore sequencing and its use in the detection of epigenetic heterogeneity in cancer.

Investigating Histone Modification Dynamics by Mechanistic Computational Modeling

The maintenance of transcriptional states regulated by histone modifications and controlled switching between these states are fundamental concepts in our understanding of nucleosome-mediated epigenetic memory. Any approach relying on genome-wide bioinformatic analyses alone offers limited scope for dissecting the molecular mechanisms involved in maintenance and switching. Mechanistic mathematical models-describing the dynamics of histone modifications at individual genomic loci-offer an alternative way to investigate these mechanisms. These models, in conjunction with quantitative experimental data-ChIP data, quantification of mRNA levels, and single-cell fluorescence tracking in clonal lineages-can generate predictions that drive more targeted experiments, allowing us to understand mechanisms that would be challenging to unravel by a purely experimental approach. In this chapter, we describe a generic stochastic modeling framework that can be used to capture histone modification dynamics and associated molecular processes-including transcription and read-write feedback by chromatin modifying complexes-at individual genomic loci. Using a specific example-transcriptional silencing by Polycomb-mediated H3K27 methylation-we demonstrate how to construct and simulate a stochastic histone modification model. We provide a step-by-step guide to programming simulations for such a model and discuss how to analyze the simulation output.

How a cell decides its own fate: a single-cell view of molecular mechanisms and dynamics of cell-type specification

On its path from a fertilized egg to one of the many cell types in a multicellular organism, a cell turns the blank canvas of its early embryonic state into a molecular profile fine-tuned to achieve a vital organismal function. This remarkable transformation emerges from the interplay between dynamically changing external signals, the cell's internal, variable state, and tremendously complex molecular machinery; we are only beginning to understand. Recently developed single-cell omics techniques have started to provide an unprecedented, comprehensive view of the molecular changes during cell-type specification and promise to reveal the underlying gene regulatory mechanism. The exponentially increasing amount of quantitative molecular data being created at the moment is slated to inform predictive, mathematical models. Such models can suggest novel ways to manipulate cell types experimentally, which has important biomedical applications. This review is meant to give the reader a starting point to participate in this exciting phase of molecular developmental biology. We first introduce some of the principal molecular players involved in cell-type specification and discuss the important organizing ability of biomolecular condensates, which has been discovered recently. We then review some of the most important single-cell omics methods and relevant findings they produced. We devote special attention to the dynamics of the molecular changes and discuss methods to measure them, most importantly lineage tracing. Finally, we introduce a conceptual framework that connects all molecular agents in a mathematical model and helps us make sense of the experimental data.

Mechanism and Therapeutic Opportunities of Histone Modifications in Chronic Liver Disease

Chronic liver disease (CLD) represents a global health problem, accounting for the heavy burden of disability and increased health care utilization. Epigenome alterations play an important role in the occurrence and progression of CLD. Histone modifications, which include acetylation, methylation, and phosphorylation, represent an essential part of epigenetic modifications that affect the transcriptional activity of genes. Different from genetic mutations, histone modifications are plastic and reversible. They can be modulated pharmacologically without changing the DNA sequence. Thus, there might be chances to establish interventional solutions by targeting histone modifications to reverse CLD. Here we summarized the roles of histone modifications in the context of alcoholic liver disease (ALD), metabolic associated fatty liver disease (MAFLD), viral hepatitis, autoimmune liver disease, drug-induced liver injury (DILI), and liver fibrosis or cirrhosis. The potential targets of histone modifications for translation into therapeutics were also investigated. In prospect, high efficacy and low toxicity drugs that are selectively targeting histone modifications are required to completely reverse CLD and prevent the development of liver cirrhosis and malignancy.

The Mesangial cell - the glomerular stromal cell

Mesangial cells are stromal cells that are important for kidney glomerular homeostasis and the glomerular response to injury. A growing body of evidence demonstrates that mesenchymal stromal cells, such as stromal fibroblasts, pericytes and vascular smooth muscle cells, not only specify the architecture of tissues but also regulate developmental processes, vascularization and cell fate specification. In addition, through crosstalk with neighbouring cells and indirectly through the remodelling of the matrix, stromal cells can regulate a variety of processes such as immunity, inflammation, regeneration and in the context of maladaptive responses - fibrosis. Insights into the molecular phenotype of kidney mesangial cells suggest that they are a specialized stromal cell of the glomerulus. Here, we review our current understanding of mesenchymal stromal cells and discuss how it informs the function of mesangial cells and their role in disease. These new insights could lead to a better understanding of kidney disease pathogenesis and the development of new therapies for chronic kidney disease.

Spatial mapping of cancer tissues by OMICS technologies

Spatial mapping of heterogeneity in gene expression in cancer tissues can improve our understanding of cancers and help in the rapid detection of cancers with high accuracy and reliability. Significant advancements have been made in recent years in OMICS technologies, which possess the strong potential to be applied in the spatial mapping of biopsy tissue samples and their molecular profiling to a single-cell level. The clinical application of OMICS technologies in spatial profiling of cancer tissues is also advancing. The current review presents recent advancements and prospects of applying OMICS technologies to the spatial mapping of various analytes in cancer tissues. We benchmark the current state of the art in the field to advance existing OMICS technologies for high throughput spatial profiling. The factors taken into consideration include spatial resolution, types of biomolecules, number of different biomolecules that can be detected from the same assay, labeled versus label-free approaches, and approximate time required for each assay. Further advancements are still needed for the widespread application of OMICs technologies in performing fast and high throughput spatial mapping of cancer tissues as well as their effective use in research and clinical applications.

Unraveling the Heterogeneity and Ontogeny of Dendritic Cells Using Single-Cell RNA Sequencing

Dendritic cells (DCs) play essential roles in innate and adaptive immunity and show high heterogeneity and intricate ontogeny. Advances in high-throughput sequencing technologies, particularly single-cell RNA sequencing (scRNA-seq), have improved the understanding of DC subsets. In this review, we discuss in detail the remarkable perspectives in DC reclassification and ontogeny as revealed by scRNA-seq. Moreover, the heterogeneity and multifunction of DCs during diseases as determined by scRNA-seq are described. Finally, we provide insights into the challenges and future trends in scRNA-seq technologies and DC research.

Intricacies of single-cell multi-omics data integration

A wealth of single-cell protocols makes it possible to characterize different molecular layers at unprecedented resolution. Integrating the resulting multimodal single-cell data to find cell-to-cell correspondences remains a challenge. We argue that data integration needs to happen at a meaningful biological level of abstraction and that it is necessary to consider the inherent discrepancies between modalities to strike a balance between biological discovery and noise removal. A survey of current methods reveals that a distinction between technical and biological origins of presumed unwanted variation between datasets is not yet commonly considered. The increasing availability of paired multimodal data will aid the development of improved methods by providing a ground truth on cell-to-cell matches.

Single-Cell RNA Sequencing Approaches for Tracing T Cell Development

T cell development occurs in the thymus, where uncommitted progenitors are directed into a range of sublineages with distinct functions. The goal is to generate a TCR repertoire diverse enough to recognize potential pathogens while remaining tolerant of self. Decades of intensive research have characterized the transcriptional programs controlling critical differentiation checkpoints at the population level. However, greater precision regarding how and when these programs orchestrate differentiation at the single-cell level is required. Single-cell RNA sequencing approaches are now being brought to bear on this question, to track the identity of cells and analyze their gene expression programs at a resolution not previously possible. In this review, we discuss recent advances in the application of these technologies that have the potential to yield unprecedented insight to T cell development.

The language of chromatin modification in human cancers

The genetic information of human cells is stored in the context of chromatin, which is subjected to DNA methylation and various histone modifications. Such a 'language' of chromatin modification constitutes a fundamental means of gene and (epi)genome regulation, underlying a myriad of cellular and developmental processes. In recent years, mounting evidence has demonstrated that miswriting, misreading or mis-erasing of the modification language embedded in chromatin represents a common, sometimes early and pivotal, event across a wide range of human cancers, contributing to oncogenesis through the induction of epigenetic, transcriptomic and phenotypic alterations. It is increasingly clear that cancer-related metabolic perturbations and oncohistone mutations also directly impact chromatin modification, thereby promoting cancerous transformation. Phase separation-based deregulation of chromatin modulators and chromatin structure is also emerging to be an important underpinning of tumorigenesis. Understanding the various molecular pathways that underscore a misregulated chromatin language in cancer, together with discovery and development of more effective drugs to target these chromatin-related vulnerabilities, will enhance treatment of human malignancies.

Molecular prospect of type-2 diabetes: Nanotechnology based diagnostics and therapeutic intervention

About ninety percent of all diabetic conditions account for T2D caused due to abnormal insulin secretion/ action or increased hepatic glucose production. Factors that contribute towards the aetiology of T2D could be well explained through biochemical, molecular, and cellular aspects. In this review, we attempt to explain the recent evolving molecular and cellular advancement associated with T2D pathophysiology. Current progress fabricated in T2D research concerning intracellular signaling cascade, inflammasome, autophagy, genetic and epigenetics changes is discretely explained in simple terms. Present available anti-diabetic therapeutic strategies commercialized and their limitations which are needed to be acknowledged are addressed in the current review. In particular, the pre-eminence of nanotechnology-based approaches to nullify the inadequacy of conventional anti-diabetic therapeutics and heterogeneous nanoparticulated systems exploited in diabetic researches are also discretely mentioned and are also listed in a tabular format in the review. Additionally, as a future prospect of nanotechnology, the review presents several strategic hypotheses to ameliorate the austerity of T2D by an engineered smart targeted nano-delivery system. In detail, an effort has been made to hypothesize novel nanotechnological based therapeutic strategies, which exploits previously described inflammasome, autophagic target points. Utilizing graphical description it is explained how a smart targeted nano-delivery system could promote β-cell growth and development by inducing the Wnt signaling pathway (inhibiting Gsk3β), inhibiting inflammasome (inhibiting NLRP3), and activating autophagic target points (protecting Atg3/Atg7 complex from oxidative stress) thereby might ameliorate the severity of T2D. Additionally, several targeting molecules associated with autophagic and epigenetic factors are also highlighted, which can be exploited in future diabetic research.

Single nucleus multi-omics regulatory landscape of the murine pituitary

To provide a multi-omics resource and investigate transcriptional regulatory mechanisms, we profile the transcriptome, chromatin accessibility, and methylation status of over 70,000 single nuclei (sn) from adult mouse pituitaries. Paired snRNAseq and snATACseq datasets from individual animals highlight a continuum between developmental epigenetically-encoded cell types and transcriptionally-determined transient cell states. Co-accessibility analysis-based identification of a putative Fshb cis-regulatory domain that overlaps the fertility-linked rs11031006 human polymorphism, followed by experimental validation illustrate the use of this resource for hypothesis generation. We also identify transcriptional and chromatin accessibility programs distinguishing each major cell type. Regulons, which are co-regulated gene sets sharing binding sites for a common transcription factor driver, recapitulate cell type clustering. We identify both cell type-specific and sex-specific regulons that are highly correlated with promoter accessibility, but not with methylation state, supporting the centrality of chromatin accessibility in shaping cell-defining transcriptional programs. The sn multi-omics atlas is accessible at snpituitaryatlas.princeton.edu.

Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy

Advances in molecular biology, microfluidics and bioinformatics have empowered the study of thousands or even millions of individual cells from malignant tumours at the single-cell level of resolution. This high-dimensional, multi-faceted characterization of the genomic, transcriptomic, epigenomic and proteomic features of the tumour and/or the associated immune and stromal cells enables the dissection of tumour heterogeneity, the complex interactions between tumour cells and their microenvironment, and the details of the evolutionary trajectory of each tumour. Single-cell transcriptomics, the ability to track individual T cell clones through paired sequencing of the T cell receptor genes and high-dimensional single-cell spatial analysis are all areas of particular relevance to immuno-oncology. Multidimensional biomarker signatures will increasingly be crucial to guiding clinical decision-making in each patient with cancer. High-dimensional single-cell technologies are likely to provide the resolution and richness of data required to generate such clinically relevant signatures in immuno-oncology. In this Perspective, we describe advances made using transformative single-cell analysis technologies, especially in relation to clinical response and resistance to immunotherapy, and discuss the growing utility of single-cell approaches for answering important research questions.

Targeted molecular profiling of rare olfactory sensory neurons identifies fate, wiring, and functional determinants

Determining the molecular properties of neurons is essential to understand their development, function and evolution. Using Targeted DamID (TaDa), we characterize RNA polymerase II occupancy and chromatin accessibility in selected Ionotropic receptor (Ir)-expressing olfactory sensory neurons in Drosophila. Although individual populations represent a minute fraction of cells, TaDa is sufficiently sensitive and specific to identify the expected receptor genes. Unique Ir expression is not consistently associated with differences in chromatin accessibility, but rather to distinct transcription factor profiles. Genes that are heterogeneously expressed across populations are enriched for neurodevelopmental factors, and we identify functions for the POU-domain protein Pdm3 as a genetic switch of Ir neuron fate, and the atypical cadherin Flamingo in segregation of neurons into discrete glomeruli. Together this study reveals the effectiveness of TaDa in profiling rare neural populations, identifies new roles for a transcription factor and a neuronal guidance molecule, and provides valuable datasets for future exploration.

Engineered TALE Repeats for Enhanced Imaging-Based Analysis of Cellular 5-Methylcytosine

Transcription-activator-like effectors (TALEs) are repeat-based, programmable DNA-binding proteins that can be engineered to recognize sequences of canonical and epigenetically modified nucleobases. Fluorescent TALEs can be used for the imaging-based analysis of cellular 5-methylcytosine (5 mC) in repetitive DNA sequences. This is based on recording fluorescence ratios from cell co-stains with two TALEs: an analytical TALE targeting the cytosine (C) position of interest through a C-selective repeat that is blocked by 5 mC, and a control TALE targeting the position with a universal repeat that binds both C and 5 mC. To enhance this approach, we report herein the development of novel 5 mC-selective repeats and their integration into TALEs that can replace universal TALEs in imaging-based 5 mC analysis, resulting in a methylation-dependent response of both TALEs. We screened a library of size-reduced repeats and identified several 5 mC binders. Compared to the 5 mC-binding repeat of natural TALEs and to the universal repeat, two repeats containing aromatic residues showed enhancement of 5 mC binding and selectivity in cellular transcription activation and electromobility shift assays, respectively. In co-stains of cellular SATIII DNA with a corresponding C-selective TALE, this selectivity results in a positive methylation response of the new TALE, offering perspectives for studying 5 mC functions in chromatin regulation by in situ imaging with increased dynamic range.

Integrated spatial genomics reveals global architecture of single nuclei

Identifying the relationships between chromosome structures, nuclear bodies, chromatin states, and gene expression is an overarching goal of nuclear organization studies^1–4. Because individual cells appear to be highly variable at all these levels⁵, it is essential to map different modalities in the same cells. Here, we report the imaging of 3,660 chromosomal loci in single mouse embryonic stem cells (mESCs) by DNA seqFISH+, along with 17 chromatin marks and subnuclear structures by sequential immunofluorescence (IF) and the expression profile of 70 RNAs. We found many loci were invariantly associated with IF marks in single mESCs. These loci form “fixed points” in the nuclear organizations in single cells and often appear on the surfaces of nuclear bodies and zones defined by combinatorial chromatin marks. Furthermore, highly expressed genes appear to be pre-positioned to active nuclear zones, independent of bursting dynamics in single cells. Our analysis also uncovered several distinct mESCs subpopulations with characteristic combinatorial chromatin states. Using clonal analysis, we show that the global levels of some chromatin marks, such as H3K27me3 and macroH2A1 (mH2A1), are heritable over at least 3–4 generations, whereas other marks fluctuate on a faster time scale. This seqFISH+ based spatial multimodal approach can be used to explore nuclear organization and cell states in diverse biological systems.

Pinpointing Cell Identity in Time and Space

Mammalian cells display a broad spectrum of phenotypes, morphologies, and functional niches within biological systems. Our understanding of mechanisms at the individual cellular level, and how cells function in concert to form tissues, organs and systems, has been greatly facilitated by centuries of extensive work to classify and characterize cell types. Classic histological approaches are now complemented with advanced single-cell sequencing and spatial transcriptomics for cell identity studies. Emerging data suggests that additional levels of information should be considered, including the subcellular spatial distribution of molecules such as RNA and protein, when classifying cells. In this Perspective piece we describe the importance of integrating cell transcriptional state with tissue and subcellular spatial and temporal information for thorough characterization of cell type and state. We refer to recent studies making use of single cell RNA-seq and/or image-based cell characterization, which highlight a need for such in-depth characterization of cell populations. We also describe the advances required in experimental, imaging and analytical methods to address these questions. This Perspective concludes by framing this argument in the context of projects such as the Human Cell Atlas, and related fields of cancer research and developmental biology.

Tools and Concepts for Interrogating and Defining Cellular Identity

Defining the mechanisms that generate specialized cell types and coordinate their functions is critical for understanding organ development and renewal. New tools and discoveries are challenging and refining our definitions of a cell type. A rapidly growing toolkit for single-cell analyses has expanded the number of markers that can be assigned to a cell simultaneously, revealing heterogeneity within cell types that were previously regarded as homogeneous populations. Additionally, cell types defined by specific molecular markers can exhibit distinct, context-dependent functions; for example, between tissues in homeostasis and those responding to damage. Here we review the current technologies used to identify and characterize cells, and we discuss how experimental and pathological perturbations are adding increasing complexity to our definitions of cell identity.

Interplay between genetics and epigenetics in osteoarthritis

Research into the molecular genetics of osteoarthritis (OA) has been substantially bolstered in the past few years by the implementation of powerful genome-wide scans that have revealed a large number of novel risk loci associated with the disease. This refreshing wave of discovery has occurred concurrently with epigenetic studies of joint tissues that have examined DNA methylation, histone modifications and regulatory RNAs. These epigenetic analyses have involved investigations of joint development, homeostasis and disease and have used both human samples and animal models. What has become apparent from a comparison of these two complementary approaches is that many OA genetic risk signals interact with, map to or correlate with epigenetic mediators. This discovery implies that epigenetic mechanisms, and their effect on gene expression, are a major conduit through which OA genetic risk polymorphisms exert their functional effects. This observation is particularly exciting as it provides mechanistic insight into OA susceptibility. Furthermore, this knowledge reveals avenues for attenuating the negative effect of risk-conferring alleles by exposing the epigenome as an exploitable target for therapeutic intervention in OA.

Designer Receptors for Nucleotide-Resolution Analysis of Genomic 5-Methylcytosine by Cellular Imaging

We report programmable receptors for the imaging‐based analysis of 5‐methylcytosine (5mC) in user‐defined DNA sequences of single cells. Using fluorescent transcription‐activator‐like effectors (TALEs) that can recognize sequences of canonical and epigenetic nucleobases through selective repeats, we imaged cellular SATIII DNA, the origin of nuclear stress bodies (nSB). We achieve high nucleobase selectivity of natural repeats in imaging and demonstrate universal nucleobase binding by an engineered repeat. We use TALE pairs differing in only one such repeat in co‐stains to detect 5mC in SATIII sequences with nucleotide resolution independently of differences in target accessibility. Further, we directly correlate the presence of heat shock factor 1 with 5mC at its recognition sequence, revealing a potential function of 5mC in its recruitment as initial step of nSB formation. This opens a new avenue for studying 5mC functions in chromatin regulation in situ with nucleotide, locus, and cell resolution.

Epigenetic control of transcriptional regulation in pluripotency and early differentiation

Pluripotent stem cells give rise to all cells of the adult organism, making them an invaluable tool in regenerative medicine. In response to differentiation cues, they can activate markedly distinct lineage-specific gene networks while turning off or rewiring pluripotency networks. Recent innovations in chromatin and nuclear structure analyses combined with classical genetics have led to novel insights into the transcriptional and epigenetic mechanisms underlying these networks. Here, we review these findings in relation to their impact on the maintenance of and exit from pluripotency and highlight the many factors that drive these processes, including histone modifying enzymes, DNA methylation and demethylation, nucleosome remodeling complexes and transcription factor-mediated enhancer switching.

The evolving concept of cell identity in the single cell era

Fueled by recent advances in single cell biology, we are moving away from qualitative and undersampled assessments of cell identity, toward building quantitative, high-resolution cell atlases. However, it remains challenging to precisely define cell identity, leading to renewed debate surrounding this concept. Here, I present three pillars that I propose are central to the notion of cell identity: phenotype, lineage and state. I explore emerging technologies that are enabling the systematic and unbiased quantification of these properties, and outline how these efforts will enable the construction of a high-resolution, dynamic landscape of cell identity, potentially revealing its underlying molecular regulation to provide new opportunities for understanding and manipulating cell fate.

Exploring single cells in space and time during tissue development, homeostasis and regeneration

Complex 3D tissues arise during development following tightly organized events in space and time. In particular, gene regulatory networks and local interactions between single cells lead to emergent properties at the tissue and organism levels. To understand the design principles of tissue organization, we need to characterize individual cells at given times, but we also need to consider the collective behavior of multiple cells across different spatial and temporal scales. In recent years, powerful single cell methods have been developed to characterize cells in tissues and to address the challenging questions of how different tissues are formed throughout development, maintained in homeostasis, and repaired after injury and disease. These approaches have led to a massive increase in data pertaining to both mRNA and protein abundances in single cells. As we review here, these new technologies, in combination with in toto live imaging, now allow us to bridge spatial and temporal information quantitatively at the single cell level and generate a mechanistic understanding of tissue development.

A periodic table of cell types

Single cell biology is currently revolutionizing developmental and evolutionary biology, revealing new cell types and states in an impressive range of biological systems. With the accumulation of data, however, the field is grappling with a central unanswered question: what exactly is a cell type? This question is further complicated by the inherently dynamic nature of developmental processes. In this Hypothesis article, we propose that a 'periodic table of cell types' can be used as a framework for distinguishing cell types from cell states, in which the periods and groups correspond to developmental trajectories and stages along differentiation, respectively. The different states of the same cell type are further analogous to 'isotopes'. We also highlight how the concept of a periodic table of cell types could be useful for predicting new cell types and states, and for recognizing relationships between cell types throughout development and evolution.

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.