Chromatin Velocity reveals epigenetic dynamics by single-cell profiling of heterochromatin and euchromatin

Cut&tag: a powerful epigenetic tool for chromatin profiling

Analysis of transcription factors and chromatin modifications at the genome-wide level provides insights into gene regulatory processes, such as transcription, cell differentiation and cellular response. Chromatin immunoprecipitation is the most popular and powerful approach for mapping chromatin, and other enzyme-tethering techniques have recently become available for living cells. Among these, Cleavage Under Targets and Tagmentation (CUT&Tag) is a relatively novel chromatin profiling method that has rapidly gained popularity in the field of epigenetics since 2019. It has also been widely adapted to map chromatin modifications and TFs in different species, illustrating the association of these chromatin epitopes with various physiological and pathological processes. Scalable single-cell CUT&Tag can be combined with distinct platforms to distinguish cellular identity, epigenetic features and even spatial chromatin profiling. In addition, CUT&Tag has been developed as a strategy for joint profiling of the epigenome, transcriptome or proteome on the same sample. In this review, we will mainly consolidate the applications of CUT&Tag and its derivatives on different platforms, give a detailed explanation of the pros and cons of this technique as well as the potential development trends and applications in the future.

Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations

Across biological systems, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. While low-dimensional dynamics can be extracted using RNA velocity, these algorithms can be fragile and rely on heuristics lacking statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. To address these challenges, we introduce a Bayesian model of RNA velocity that couples velocity field and manifold estimation in a reformulated, unified framework, identifying the parameters of an explicit dynamical system. Focusing on the cell cycle, we implement VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validate its ability to infer cell cycle periods using live imaging. We also apply VeloCycle to reveal speed differences in regionally defined progenitors and Perturb-seq gene knockdowns. Overall, VeloCycle expands the single-cell RNA sequencing analysis toolkit with a modular and statistically consistent RNA velocity inference framework.

R-loops' m6A modification and its roles in cancers

R-loops are three-stranded nucleic acid structures composed of an RNA-DNA hybrid and a displaced DNA strand. They are widespread and play crucial roles in regulating gene expression, DNA replication, and DNA and histone modifications. However, their regulatory mechanisms remain unclear. As R-loop detection technology advances, changes in R-loop levels have been observed in cancer models, often associated with transcription-replication conflicts and genomic instability. N6-methyladenosine (m6A) is an RNA epigenetic modification that regulates gene expression by affecting RNA localization, splicing, translation, and degradation. Upon reviewing the literature, we found that R-loops with m6A modifications are implicated in tumor development and progression. This article summarizes the molecular mechanisms and detection methods of R-loops and m6A modifications in gene regulation, and reviews recent research on m6A-modified R-loops in oncology. Our goal is to provide new insights into the origins of genomic instability in cancer and potential strategies for targeted therapy.

Epigenomic heterogeneity as a source of tumour evolution

In the past decade, remarkable progress in cancer medicine has been achieved by the development of treatments that target DNA sequence variants. However, a purely genetic approach to treatment selection is hampered by the fact that diverse cell states can emerge from the same genotype. In multicellular organisms, cell-state heterogeneity is driven by epigenetic processes that regulate DNA-based functions such as transcription; disruption of these processes is a hallmark of cancer that enables the emergence of defective cell states. Advances in single-cell technologies have unlocked our ability to quantify the epigenomic heterogeneity of tumours and understand its mechanisms, thereby transforming our appreciation of how epigenomic changes drive cancer evolution. This Review explores the idea that epigenomic heterogeneity and plasticity act as a reservoir of cell states and therefore as a source of tumour evolution. Best practices to quantify epigenomic heterogeneity and explore its various causes and consequences are discussed, including epigenomic reprogramming, stochastic changes and lasting memory. The design of new therapeutic approaches to restrict epigenomic heterogeneity, with the long-term objective of limiting cancer development and progression, is also addressed.

Functional analysis of cell plasticity using single-cell technologies

Metazoan organisms are heterocellular systems composed of hundreds of different cell types, which arise from an isogenic genome through differentiation. Cellular 'plasticity' further enables cells to alter their fate in response to exogenous cues and is involved in a variety of processes, such as wound healing, infection, and cancer. Recent advances in cellular model systems, high-dimensional single-cell technologies, and lineage tracing have sparked a renaissance in plasticity research. Here, we discuss the definition of cell plasticity, evaluate state-of-the-art model systems and techniques to study cell-fate dynamics, and explore the application of single-cell technologies to obtain functional insights into cell plasticity in healthy and diseased tissues. The integration of advanced biomimetic model systems, single-cell technologies, and high-throughput perturbation studies is enabling a new era of research into non-genetic plasticity in metazoan systems.

Single cell genome and epigenome co-profiling reveals hardwiring and plasticity in breast cancer

Understanding the impact of genetic alterations on epigenomic phenotypes during breast cancer progression is challenging with unimodal measurements. Here, we report wellDA-seq, the first high-genomic resolution, high-throughput method that can simultaneously measure the whole genome and chromatin accessibility profiles of thousands of single cells. Using wellDA-seq, we profiled 22,123 single cells from 2 normal and 9 tumors breast tissues. By directly mapping the epigenomic phenotypes to genetic lineages across cancer subclones, we found evidence of both genetic hardwiring and epigenetic plasticity. In 6 estrogen-receptor positive breast cancers, we directly identified the ancestral cancer cells, and found that their epithelial cell-of-origin was Luminal Hormone Responsive cells. We also identified cell types with copy number aberrations (CNA) in normal breast tissues and discovered non-epithelial cell types in the microenvironment with CNAs in breast cancers. These data provide insights into the complex relationship between genetic alterations and epigenomic phenotypes during breast tumor evolution.

Progress in multifactorial single-cell chromatin profiling methods

Chromatin states play a key role in shaping overall cellular states and fates. Building a complete picture of the functional state of chromatin in cells requires the co-detection of several distinct biochemical aspects. These span DNA methylation, chromatin accessibility, chromosomal conformation, histone posttranslational modifications, and more. While this certainly presents a challenging task, over the past few years many new and creative methods have been developed that now enable co-assay of these different aspects of chromatin at single cell resolution. This field is entering an exciting phase, where a confluence of technological improvements, decreased sequencing costs, and computational innovation are presenting new opportunities to dissect the diversity of chromatin states present in tissues, and how these states may influence gene regulation. In this review, I discuss the spectrum of current experimental approaches for multifactorial chromatin profiling, highlight some of the experimental and analytical challenges, as well as some areas for further innovation.

Image-based discrimination of the early stages of mesenchymal stem cell differentiation

Mesenchymal stem cells (MSCs) are self-renewing, multipotent cells, which can be used in cellular and tissue therapeutics. MSCs cell number can be expanded in vitro, but premature differentiation results in reduced cell number and compromised therapeutic efficacies. Current techniques fail to discriminate the "stem-like" population from early stages (12 h) of differentiated MSC population. Here, we imaged nuclear structure and actin architecture using immunofluorescence and used deep learning-based computer vision technology to discriminate the early stages (6-12 h) of MSC differentiation. Convolutional neural network models trained by nucleus and actin images have high accuracy in reporting MSC differentiation; nuclear images alone can identify early stages of differentiation. Concurrently, we show that chromatin fluidity and heterochromatin levels or localization change during early MSC differentiation. This study quantifies changes in cell architecture during early MSC differentiation and describes a novel image-based diagnostic tool that could be widely used in MSC culture, expansion and utilization.

Emerging toolkits for decoding the co-occurrence of modified histones and chromatin proteins

In eukaryotes, DNA is packaged into chromatin with the help of highly conserved histone proteins. Together with DNA-binding proteins, posttranslational modifications (PTMs) on these histones play crucial roles in regulating genome function, cell fate determination, inheritance of acquired traits, cellular states, and diseases. While most studies have focused on individual DNA-binding proteins, chromatin proteins, or histone PTMs in bulk cell populations, such chromatin features co-occur and potentially act cooperatively to accomplish specific functions in a given cell. This review discusses state-of-the-art techniques for the simultaneous profiling of multiple chromatin features in low-input samples and single cells, focusing on histone PTMs, DNA-binding, and chromatin proteins. We cover the origins of the currently available toolkits, compare and contrast their characteristic features, and discuss challenges and perspectives for future applications. Studying the co-occurrence of histone PTMs, DNA-binding proteins, and chromatin proteins in single cells will be central for a better understanding of the biological relevance of combinatorial chromatin features, their impact on genomic output, and cellular heterogeneity.

Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data

Gene set scoring (GSS) has been routinely conducted for gene expression analysis of bulk or single-cell RNA sequencing (RNA-seq) data, which helps to decipher single-cell heterogeneity and cell type-specific variability by incorporating prior knowledge from functional gene sets. Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a powerful technique for interrogating single-cell chromatin-based gene regulation, and genes or gene sets with dynamic regulatory potentials can be regarded as cell type-specific markers as if in single-cell RNA-seq (scRNA-seq). However, there are few GSS tools specifically designed for scATAC-seq, and the applicability and performance of RNA-seq GSS tools on scATAC-seq data remain to be investigated. Here, we systematically benchmarked ten GSS tools, including four bulk RNA-seq tools, five scRNA-seq tools, and one scATAC-seq method. First, using matched scATAC-seq and scRNA-seq datasets, we found that the performance of GSS tools on scATAC-seq data was comparable to that on scRNA-seq, suggesting their applicability to scATAC-seq. Then, the performance of different GSS tools was extensively evaluated using up to ten scATAC-seq datasets. Moreover, we evaluated the impact of gene activity conversion, dropout imputation, and gene set collections on the results of GSS. Results show that dropout imputation can significantly promote the performance of almost all GSS tools, while the impact of gene activity conversion methods or gene set collections on GSS performance is more dependent on GSS tools or datasets. Finally, we provided practical guidelines for choosing appropriate preprocessing methods and GSS tools in different application scenarios.

Scalable integration of multiomic single-cell data using generative adversarial networks

MOTIVATION

Single-cell profiling has become a common practice to investigate the complexity of tissues, organs, and organisms. Recent technological advances are expanding our capabilities to profile various molecular layers beyond the transcriptome such as, but not limited to, the genome, the epigenome, and the proteome. Depending on the experimental procedure, these data can be obtained from separate assays or the very same cells. Yet, integration of more than two assays is currently not supported by the majority of the computational frameworks avaiable.

RESULTS

We here propose a Multi-Omic data integration framework based on Wasserstein Generative Adversarial Networks suitable for the analysis of paired or unpaired data with a high number of modalities (>2). At the core of our strategy is a single network trained on all modalities together, limiting the computational burden when many molecular layers are evaluated.

AVAILABILITY AND IMPLEMENTATION

Source code of our framework is available at https://github.com/vgiansanti/MOWGAN.

Understanding glioblastoma at the single-cell level: Recent advances and future challenges

Glioblastoma, the most aggressive and prevalent form of primary brain tumor, is characterized by rapid growth, diffuse infiltration, and resistance to therapies. Intrinsic heterogeneity and cellular plasticity contribute to its rapid progression under therapy; therefore, there is a need to fully understand these tumors at a single-cell level. Over the past decade, single-cell transcriptomics has enabled the molecular characterization of individual cells within glioblastomas, providing previously unattainable insights into the genetic and molecular features that drive tumorigenesis, disease progression, and therapy resistance. However, despite advances in single-cell technologies, challenges such as high costs, complex data analysis and interpretation, and difficulties in translating findings into clinical practice persist. As single-cell technologies are developed further, more insights into the cellular and molecular heterogeneity of glioblastomas are expected, which will help guide the development of personalized and effective therapies, thereby improving prognosis and quality of life for patients.

A dynamical perspective: moving towards mechanism in single-cell transcriptomics

As the field of single-cell transcriptomics matures, research is shifting focus from phenomenological descriptions of cellular phenotypes to a mechanistic understanding of the gene regulation underneath. This perspective considers the value of capturing dynamical information at single-cell resolution for gaining mechanistic insight; reviews the available technologies for recording and inferring temporal information in single cells; and explores whether better dynamical resolution is sufficient to adequately capture the causal relationships driving complex biological systems. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

Nano-CUT&Tag for multimodal chromatin profiling at single-cell resolution

The ability to comprehensively analyze the chromatin state with single-cell resolution is crucial for understanding gene regulatory principles in heterogenous tissues or during development. Recently, we developed a nanobody-based single-cell CUT&Tag (nano-CT) protocol to simultaneously profile three epigenetic modalities-two histone marks and open chromatin state-from the same single cell. Nano-CT implements a new set of secondary nanobody-Tn5 fusion proteins to direct barcoded tagmentation by Tn5 transposase to genomic targets labeled by primary antibodies raised in different species. Such nanobody-Tn5 fusion proteins are currently not commercially available, and their in-house production and purification can be completed in 3-4 d by following our detailed protocol. The single-cell indexing in nano-CT is performed on a commercially available platform, making it widely accessible to the community. In comparison to other multimodal methods, nano-CT stands out in data complexity, low sample requirements and the flexibility to choose two of the three modalities. In addition, nano-CT works efficiently with fresh brain samples, generating multimodal epigenomic profiles for thousands of brain cells at single-cell resolution. The nano-CT protocol can be completed in just 3 d by users with basic skills in standard molecular biology and bioinformatics, although previous experience with single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) is beneficial for more in-depth data analysis. As a multimodal assay, nano-CT holds immense potential to reveal interactions of various chromatin modalities, to explore epigenetic heterogeneity and to increase our understanding of the role and interplay that chromatin dynamics has in cellular development.

Representing and extracting knowledge from single-cell data

Single-cell analysis is currently one of the most high-resolution techniques to study biology. The large complex datasets that have been generated have spurred numerous developments in computational biology, in particular the use of advanced statistics and machine learning. This review attempts to explain the deeper theoretical concepts that underpin current state-of-the-art analysis methods. Single-cell analysis is covered from cell, through instruments, to current and upcoming models. The aim of this review is to spread concepts which are not yet in common use, especially from topology and generative processes, and how new statistical models can be developed to capture more of biology. This opens epistemological questions regarding our ontology and models, and some pointers will be given to how natural language processing (NLP) may help overcome our cognitive limitations for understanding single-cell data.

Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations

Across a range of biological processes, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. Single-cell RNA-sequencing (scRNA-seq) only measures temporal snapshots of gene expression. However, information on the underlying low-dimensional dynamics can be extracted using RNA velocity, which models unspliced and spliced RNA abundances to estimate the rate of change of gene expression. Available RNA velocity algorithms can be fragile and rely on heuristics that lack statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. Here, we develop a generative model of RNA velocity and a Bayesian inference approach that solves these problems. Our model couples velocity field and manifold estimation in a reformulated, unified framework, so as to coherently identify the parameters of an autonomous dynamical system. Focusing on the cell cycle, we implemented VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validated using live-imaging its ability to infer actual cell cycle periods. We benchmarked RNA velocity inference with sensitivity analyses and demonstrated one- and multiple-sample testing. We also conducted Markov chain Monte Carlo inference on the model, uncovering key relationships between gene-specific kinetics and our gene-independent velocity estimate. Finally, we applied VeloCycle to in vivo samples and in vitro genome-wide Perturb-seq, revealing regionally-defined proliferation modes in neural progenitors and the effect of gene knockdowns on cell cycle speed. Ultimately, VeloCycle expands the scRNA-seq analysis toolkit with a modular and statistically rigorous RNA velocity inference framework.

DeepVelo: deep learning extends RNA velocity to multi-lineage systems with cell-specific kinetics

Existing RNA velocity estimation methods strongly rely on predefined dynamics and cell-agnostic constant transcriptional kinetic rates, assumptions often violated in complex and heterogeneous single-cell RNA sequencing (scRNA-seq) data. Using a graph convolution network, DeepVelo overcomes these limitations by generalizing RNA velocity to cell populations containing time-dependent kinetics and multiple lineages. DeepVelo infers time-varying cellular rates of transcription, splicing, and degradation, recovers each cell's stage in the differentiation process, and detects functionally relevant driver genes regulating these processes. Application to various developmental and pathogenic processes demonstrates DeepVelo's capacity to study complex differentiation and lineage decision events in heterogeneous scRNA-seq data.

Cancer Evolution: A Multifaceted Affair

Cancer cells adapt and survive through the acquisition and selection of molecular modifications. This process defines cancer evolution. Building on a theoretical framework based on heritable genetic changes has provided insights into the mechanisms supporting cancer evolution. However, cancer hallmarks also emerge via heritable nongenetic mechanisms, including epigenetic and chromatin topological changes, and interactions between tumor cells and the tumor microenvironment. Recent findings on tumor evolutionary mechanisms draw a multifaceted picture where heterogeneous forces interact and influence each other while shaping tumor progression. A comprehensive characterization of the cancer evolutionary toolkit is required to improve personalized medicine and biomarker discovery.

SIGNIFICANCE

Tumor evolution is fueled by multiple enabling mechanisms. Importantly, genetic instability, epigenetic reprogramming, and interactions with the tumor microenvironment are neither alternative nor independent evolutionary mechanisms. As demonstrated by findings highlighted in this perspective, experimental and theoretical approaches must account for multiple evolutionary mechanisms and their interactions to ultimately understand, predict, and steer tumor evolution.

Single-cell joint profiling of multiple epigenetic proteins and gene transcription

Sculpting the epigenome with a combination of histone modifications and transcription factor occupancy determines gene transcription and cell fate specification. Here, we first develop uCoTarget, utilizing a split-pool barcoding strategy for realizing ultrahigh-throughput single-cell joint profiling of multiple epigenetic proteins. Through extensive optimization for sensitivity and multimodality resolution, we demonstrate that uCoTarget enables simultaneous detection of five histone modifications (H3K27ac, H3K4me3, H3K4me1, H3K36me3, and H3K27me3) in 19,860 single cells. We applied uCoTarget to the in vitro generation of hematopoietic stem/progenitor cells (HSPCs) from human embryonic stem cells, presenting multimodal epigenomic profiles in 26,418 single cells. uCoTarget reveals establishment of pairing of HSPC enhancers (H3K27ac) and promoters (H3K4me3) and RUNX1 engagement priming for H3K27ac activation along the HSPC path. We then develop uCoTargetX, an expansion of uCoTarget to simultaneously measure transcriptome and multiple epigenome targets. Together, our methods enable generalizable, versatile multimodal profiles for reconstructing comprehensive epigenome and transcriptome landscapes and analyzing the regulatory interplay at single-cell level.

ENTRAIN: integrating trajectory inference and gene regulatory networks with spatial data to co-localize the receptor-ligand interactions that specify cell fate

MOTIVATION

Cell fate is commonly studied by profiling the gene expression of single cells to infer developmental trajectories based on expression similarity, RNA velocity, or statistical mechanical properties. However, current approaches do not recover microenvironmental signals from the cellular niche that drive a differentiation trajectory.

RESULTS

We resolve this with environment-aware trajectory inference (ENTRAIN), a computational method that integrates trajectory inference methods with ligand-receptor pair gene regulatory networks to identify extracellular signals and evaluate their relative contribution towards a differentiation trajectory. The output from ENTRAIN can be superimposed on spatial data to co-localize cells and molecules in space and time to map cell fate potentials to cell-cell interactions. We validate and benchmark our approach on single-cell bone marrow and spatially resolved embryonic neurogenesis datasets to identify known and novel environmental drivers of cellular differentiation.

AVAILABILITY AND IMPLEMENTATION

ENTRAIN is available as a public package at https://github.com/theimagelab/entrain and can be used on both single-cell and spatially resolved datasets.

Gene regulatory network inference in the era of single-cell multi-omics

The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory networks (GRNs). The study of GRNs is useful to understand how cellular identity is established, maintained and disrupted in disease. GRNs can be inferred from experimental data - historically, bulk omics data - and/or from the literature. The advent of single-cell multi-omics technologies has led to the development of novel computational methods that leverage genomic, transcriptomic and chromatin accessibility information to infer GRNs at an unprecedented resolution. Here, we review the key principles of inferring GRNs that encompass transcription factor-gene interactions from transcriptomics and chromatin accessibility data. We focus on the comparison and classification of methods that use single-cell multimodal data. We highlight challenges in GRN inference, in particular with respect to benchmarking, and potential further developments using additional data modalities.

Single-cell sequencing technology applied to epigenetics for the study of tumor heterogeneity

BACKGROUND

Previous studies have traditionally attributed the initiation of cancer cells to genetic mutations, considering them as the fundamental drivers of carcinogenesis. However, recent research has shed light on the crucial role of epigenomic alterations in various cell types present within the tumor microenvironment, suggesting their potential contribution to tumor formation and progression. Despite these significant findings, the progress in understanding the epigenetic mechanisms regulating tumor heterogeneity has been impeded over the past few years due to the lack of appropriate technical tools and methodologies.

RESULTS

The emergence of single-cell sequencing has enhanced our understanding of the epigenetic mechanisms governing tumor heterogeneity by revealing the distinct epigenetic layers of individual cells (chromatin accessibility, DNA/RNA methylation, histone modifications, nucleosome localization) and the diverse omics (transcriptomics, genomics, multi-omics) at the single-cell level. These technologies provide us with new insights into the molecular basis of intratumoral heterogeneity and help uncover key molecular events and driving mechanisms in tumor development.

CONCLUSION

This paper provides a comprehensive review of the emerging analytical and experimental approaches of single-cell sequencing in various omics, focusing specifically on epigenomics. These approaches have the potential to capture and integrate multiple dimensions of individual cancer cells, thereby revealing tumor heterogeneity and epigenetic features. Additionally, this paper outlines the future trends of these technologies and their current technical limitations.

Decoding Human Biology and Disease Using Single-cell Omics Technologies

Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.

Exploring New Dimensions of Tumor Heterogeneity: The Application of Single Cell Analysis to Organoid-Based 3D In Vitro Models

Modeling the heterogeneity of the tumor microenvironment (TME) in vitro is essential to investigating fundamental cancer biology and developing novel treatment strategies that holistically address the factors affecting tumor progression and therapeutic response. Thus, the development of new tools for both in vitro modeling, such as patient-derived organoids (PDOs) and complex 3D in vitro models, and single cell omics analysis, such as single-cell RNA-sequencing, represents a new frontier for investigating tumor heterogeneity. Specifically, the integration of PDO-based 3D in vitro models and single cell analysis offers a unique opportunity to explore the intersecting effects of interpatient, microenvironmental, and tumor cell heterogeneity on cell phenotypes in the TME. In this review, the current use of PDOs in complex 3D in vitro models of the TME is discussed and the emerging directions in the development of these models are highlighted. Next, work that has successfully applied single cell analysis to PDO-based models is examined and important experimental considerations are identified for this approach. Finally, open questions are highlighted that may be amenable to exploration using the integration of PDO-based models and single cell analysis. Ultimately, such investigations may facilitate the identification of novel therapeutic targets for cancer that address the significant influence of tumor-TME interactions.

The technological landscape and applications of single-cell multi-omics

Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.

Evolutionary fingerprints of EMT in pancreatic cancers

Mesenchymal plasticity has been extensively described in advanced and metastatic epithelial cancers; however, its functional role in malignant progression, metastatic dissemination and therapy response is controversial. More importantly, the role of epithelial mesenchymal transition (EMT) and cell plasticity in tumor heterogeneity, clonal selection and clonal evolution is poorly understood. Functionally, our work clarifies the contribution of EMT to malignant progression and metastasis in pancreatic cancer. We leveraged ad hoc somatic mosaic genome engineering, lineage tracing and ablation technologies and dynamic genetic reporters to trace and ablate tumor-specific lineages along the phenotypic spectrum of epithelial to mesenchymal plasticity. The experimental evidences clarify the essential contribution of mesenchymal lineages to pancreatic cancer evolution and metastatic dissemination. Spatial genomic analysis combined with single cell transcriptomic and epigenomic profiling of epithelial and mesenchymal lineages reveals that EMT promotes with the emergence of chromosomal instability (CIN). Specifically tumor lineages with mesenchymal features display highly conserved patterns of genomic evolution including complex structural genomic rearrangements and chromotriptic events. Genetic ablation of mesenchymal lineages robustly abolished these mutational processes and evolutionary patterns, as confirmed by cross species analysis of pancreatic and other human epithelial cancers. Mechanistically, we discovered that malignant cells with mesenchymal features display increased chromatin accessibility, particularly in the pericentromeric and centromeric regions, which in turn results in delayed mitosis and catastrophic cell division. Therefore, EMT favors the emergence of high-fitness tumor cells, strongly supporting the concept of a cell-state, lineage-restricted patterns of evolution, where cancer cell sub-clonal speciation is propagated to progenies only through restricted functional compartments. Restraining those evolutionary routes through genetic ablation of clones capable of mesenchymal plasticity and extinction of the derived lineages completely abrogates the malignant potential of one of the most aggressive form of human cancer.

Single-cell multiomic understanding of HIV-1 reservoir at epigenetic, transcriptional, and protein levels

PURPOSE OF REVIEW

The success of HIV-1 eradication strategies relies on in-depth understanding of HIV-1-infected cells. However, HIV-1-infected cells are extremely heterogeneous and rare. Single-cell multiomic approaches resolve the heterogeneity and rarity of HIV-1-infected cells.

RECENT FINDINGS

Advancement in single-cell multiomic approaches enabled HIV-1 reservoir profiling across the epigenetic (ATAC-seq), transcriptional (RNA-seq), and protein levels (CITE-seq). Using HIV-1 RNA as a surrogate, ECCITE-seq identified enrichment of HIV-1-infected cells in clonally expanded cytotoxic CD4+ T cells. Using HIV-1 DNA PCR-activated microfluidic sorting, FIND-seq captured the bulk transcriptome of HIV-1 DNA+ cells. Using targeted HIV-1 DNA amplification, PheP-seq identified surface protein expression of intact versus defective HIV-1-infected cells. Using ATAC-seq to identify HIV-1 DNA, ASAP-seq captured transcription factor activity and surface protein expression of HIV-1 DNA+ cells. Combining HIV-1 mapping by ATAC-seq and HIV-1 RNA mapping by RNA-seq, DOGMA-seq captured the epigenetic, transcriptional, and surface protein expression of latent and transcriptionally active HIV-1-infected cells. To identify reproducible biological insights and authentic HIV-1-infected cells and avoid false-positive discovery of artifacts, we reviewed current practices of single-cell multiomic experimental design and bioinformatic analysis.

SUMMARY

Single-cell multiomic approaches may identify innovative mechanisms of HIV-1 persistence, nominate therapeutic strategies, and accelerate discoveries.

Methods and applications for single-cell and spatial multi-omics

The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome from single cells is transforming our understanding of cell biology in health and disease. In less than a decade, the field has seen tremendous technological revolutions that enable crucial new insights into the interplay between intracellular and intercellular molecular mechanisms that govern development, physiology and pathogenesis. In this Review, we highlight advances in the fast-developing field of single-cell and spatial multi-omics technologies (also known as multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers. We demonstrate their impact on fundamental cell biology and translational research, discuss current challenges and provide an outlook to the future.

Nanobody-tethered transposition enables multifactorial chromatin profiling at single-cell resolution

Chromatin states are functionally defined by a complex combination of histone modifications, transcription factor binding, DNA accessibility and other factors. Current methods for defining chromatin states cannot measure more than one aspect in a single experiment at single-cell resolution. Here we introduce nanobody-tethered transposition followed by sequencing (NTT-seq), an assay capable of measuring the genome-wide presence of up to three histone modifications and protein-DNA binding sites at single-cell resolution. NTT-seq uses recombinant Tn5 transposase fused to a set of secondary nanobodies (nb). Each nb-Tn5 fusion protein specifically binds to different immunoglobulin-G antibodies, enabling a mixture of primary antibodies binding different epitopes to be used in a single experiment. We apply bulk-cell and single-cell NTT-seq to generate high-resolution multimodal maps of chromatin states in cell culture and in human immune cells. We also extend NTT-seq to enable simultaneous profiling of cell surface protein expression and multimodal chromatin states to study cells of the immune system.

InferLoop: leveraging single-cell chromatin accessibility for the signal of chromatin loop

Deciphering cell-type-specific 3D structures of chromatin is challenging. Here, we present InferLoop, a novel method for inferring the strength of chromatin interaction using single-cell chromatin accessibility data. The workflow of InferLoop is, first, to conduct signal enhancement by grouping nearby cells into bins, and then, for each bin, leverage accessibility signals for loop signals using a newly constructed metric that is similar to the perturbation of the Pearson correlation coefficient. In this study, we have described three application scenarios of InferLoop, including the inference of cell-type-specific loop signals, the prediction of gene expression levels and the interpretation of intergenic loci. The effectiveness and superiority of InferLoop over other methods in those three scenarios are rigorously validated by using the single-cell 3D genome structure data of human brain cortex and human blood, the single-cell multi-omics data of human blood and mouse brain cortex, and the intergenic loci in the GWAS Catalog database as well as the GTEx database, respectively. In addition, InferLoop can be applied to predict loop signals of individual spots using the spatial chromatin accessibility data of mouse embryo. InferLoop is available at https://github.com/jumphone/inferloop.

Multifactorial profiling of epigenetic landscapes at single-cell resolution using MulTI-Tag

Chromatin profiling at locus resolution uncovers gene regulatory features that define cell types and developmental trajectories, but it remains challenging to map and compare different chromatin-associated proteins in the same sample. Here we describe Multiple Target Identification by Tagmentation (MulTI-Tag), an antibody barcoding approach for profiling multiple chromatin features simultaneously in single cells. We optimized MulTI-Tag to retain high sensitivity and specificity, and we demonstrate detection of up to three histone modifications in the same cell: H3K27me3, H3K4me1/2 and H3K36me3. We apply MulTI-Tag to resolve distinct cell types and developmental trajectories; to distinguish unique, coordinated patterns of active and repressive element regulatory usage associated with differentiation outcomes; and to uncover associations between histone marks. Multifactorial epigenetic profiling holds promise for comprehensively characterizing cell-specific gene regulatory landscapes in development and disease.

Patient-derived xenograft models in cancer therapy: technologies and applications

Patient-derived xenograft (PDX) models, in which tumor tissues from patients are implanted into immunocompromised or humanized mice, have shown superiority in recapitulating the characteristics of cancer, such as the spatial structure of cancer and the intratumor heterogeneity of cancer. Moreover, PDX models retain the genomic features of patients across different stages, subtypes, and diversified treatment backgrounds. Optimized PDX engraftment procedures and modern technologies such as multi-omics and deep learning have enabled a more comprehensive depiction of the PDX molecular landscape and boosted the utilization of PDX models. These irreplaceable advantages make PDX models an ideal choice in cancer treatment studies, such as preclinical trials of novel drugs, validating novel drug combinations, screening drug-sensitive patients, and exploring drug resistance mechanisms. In this review, we gave an overview of the history of PDX models and the process of PDX model establishment. Subsequently, the review presents the strengths and weaknesses of PDX models and highlights the integration of novel technologies in PDX model research. Finally, we delineated the broad application of PDX models in chemotherapy, targeted therapy, immunotherapy, and other novel therapies.

Analyzing genomic and epigenetic profiles in single cells by hybrid transposase (scGET-seq)

scGET-seq simultaneously profiles euchromatin and heterochromatin. scGET-seq exploits the concurrent action of transposase Tn5 and its hybrid form TnH, which targets H3K9me3 domains. Here we present a step-by-step protocol to profile single cells by scGET-seq using a 10× Chromium Controller. We describe steps for transposomes preparation and validation. We detail nuclei preparation and transposition, followed by encapsulation, library preparation, sequencing, and data analysis. For complete details on the use and execution of this protocol, please refer to Tedesco et al. (2022)1 and de Pretis and Cittaro (2022).2.

Single-cell and long-read sequencing to enhance modelling of splicing and cell-fate determination

Single-cell sequencing technologies have revolutionised the life sciences and biomedical research. Single-cell sequencing provides high-resolution data on cell heterogeneity, allowing high-fidelity cell type identification, and lineage tracking. Computational algorithms and mathematical models have been developed to make sense of the data, compensate for errors and simulate the biological processes, which has led to breakthroughs in our understanding of cell differentiation, cell-fate determination and tissue cell composition. The development of long-read (a.k.a. third-generation) sequencing technologies has produced powerful tools for investigating alternative splicing, isoform expression (at the RNA level), genome assembly and the detection of complex structural variants (at the DNA level). In this review, we provide an overview of the recent advancements in single-cell and long-read sequencing technologies, with a particular focus on the computational algorithms that help in correcting, analysing, and interpreting the resulting data. Additionally, we review some mathematical models that use single-cell and long-read sequencing data to study cell-fate determination and alternative splicing, respectively. Moreover, we highlight the emerging opportunities in modelling cell-fate determination that result from the combination of single-cell and long-read sequencing technologies.

Multi-omic single-cell velocity models epigenome-transcriptome interactions and improves cell fate prediction

Multi-omic single-cell datasets, in which multiple molecular modalities are profiled within the same cell, offer an opportunity to understand the temporal relationship between epigenome and transcriptome. To realize this potential, we developed MultiVelo, a differential equation model of gene expression that extends the RNA velocity framework to incorporate epigenomic data. MultiVelo uses a probabilistic latent variable model to estimate the switch time and rate parameters of chromatin accessibility and gene expression and improves the accuracy of cell fate prediction compared to velocity estimates from RNA only. Application to multi-omic single-cell datasets from brain, skin and blood cells reveals two distinct classes of genes distinguished by whether chromatin closes before or after transcription ceases. We also find four types of cell states: two states in which epigenome and transcriptome are coupled and two distinct decoupled states. Finally, we identify time lags between transcription factor expression and binding site accessibility and between disease-associated SNP accessibility and expression of the linked genes. MultiVelo is available on PyPI, Bioconda and GitHub ( https://github.com/welch-lab/MultiVelo ).

Tn5 DNA Transposase in Multi-Omics Research

Tn5 transposase use in biotechnology has substantially advanced the sequencing applications of genome-wide analysis of cells. This is mainly due to the ability of Tn5 transposase to efficiently transpose DNA essentially randomly into any target DNA without the aid of other factors. This concise review is focused on the advances in Tn5 applications in multi-omics technologies, genome-wide profiling, and Tn5 hybrid molecule creation. The possibilities of other transposase uses are also discussed.

NF-κB dynamics in the language of immune cells

Biological discovery has been driven by advances in throughput and resolution of analysis technologies. They have also created an indelible bias for snapshot-based knowledge. Even though recent methods such as multi-omics single-cell assays have empowered immunological investigations, they still provide snapshots of cellular behaviors and thus, have inherent limitations in reconstructing unsynchronized dynamic events across individual cells. Here, we present a rationale for how NF-κB may convey specificity of contextual information through subtle quantitative features of its signaling dynamics. The next frontier of predictive understanding should involve functional characterization of NF-κB signaling dynamics and their immunological implications. This may help solve the apparent paradox that a ubiquitously activated transcription factor can shape accurate responses to different immune challenges.

Characterizing cis-regulatory elements using single-cell epigenomics

Cell type-specific gene expression patterns and dynamics during development or in disease are controlled by cis-regulatory elements (CREs), such as promoters and enhancers. Distinct classes of CREs can be characterized by their epigenomic features, including DNA methylation, chromatin accessibility, combinations of histone modifications and conformation of local chromatin. Tremendous progress has been made in cataloguing CREs in the human genome using bulk transcriptomic and epigenomic methods. However, single-cell epigenomic and multi-omic technologies have the potential to provide deeper insight into cell type-specific gene regulatory programmes as well as into how they change during development, in response to environmental cues and through disease pathogenesis. Here, we highlight recent advances in single-cell epigenomic methods and analytical tools and discuss their readiness for human tissue profiling.

Multimodal chromatin profiling using nanobody-based single-cell CUT&Tag

Probing histone modifications at a single-cell level in thousands of cells has been enabled by technologies such as single-cell CUT&Tag. Here we describe nano-CUT&Tag (nano-CT), which allows simultaneous mapping of up to three epigenomic modalities at single-cell resolution using nanobody-Tn5 fusion proteins. Multimodal nano-CT is compatible with starting materials as low as 25,000–200,000 cells and has significantly higher sensitivity and number of fragments per cell than single-cell CUT&Tag. We use nano-CT to simultaneously profile chromatin accessibility, H3K27ac, and H3K27me3 in juvenile mouse brain, allowing for discrimination of more cell types and states than unimodal single-cell CUT&Tag. We also infer chromatin velocity between assay for transposase-accessible chromatin (ATAC) and H3K27ac in the oligodendrocyte lineage and deconvolute H3K27me3 repressive states, finding two sequential waves of H3K27me3 repression at distinct gene modules during oligodendrocyte lineage progression. Given its high resolution, versatility, and multimodal features, nano-CT allows unique insights in epigenetic landscapes in complex biological systems at the single-cell level.

Neural precursor cells tune striatal connectivity through the release of IGFBPL1

The adult brain retains over life endogenous neural stem/precursor cells (eNPCs) within the subventricular zone (SVZ). Whether or not these cells exert physiological functions is still unclear. In the present work, we provide evidence that SVZ-eNPCs tune structural, electrophysiological, and behavioural aspects of striatal function via secretion of insulin-like growth factor binding protein-like 1 (IGFBPL1). In mice, selective ablation of SVZ-eNPCs or selective abrogation of IGFBPL1 determined an impairment of striatal medium spiny neuron morphology, a higher failure rate in GABAergic transmission mediated by fast-spiking interneurons, and striatum-related behavioural dysfunctions. We also found IGFBPL1 expression in the human SVZ, foetal and induced-pluripotent stem cell-derived NPCs. Finally, we found a significant correlation between SVZ damage, reduction of striatum volume, and impairment of information processing speed in neurological patients. Our results highlight the physiological role of adult SVZ-eNPCs in supporting cognitive functions by regulating striatal neuronal activity.

Chromatin accessibility: methods, mechanisms, and biological insights

Access to DNA is a prerequisite to the execution of essential cellular processes that include transcription, replication, chromosomal segregation, and DNA repair. How the proteins that regulate these processes function in the context of chromatin and its dynamic architectures is an intensive field of study. Over the past decade, genome-wide assays and new imaging approaches have enabled a greater understanding of how access to the genome is regulated by nucleosomes and associated proteins. Additional mechanisms that may control DNA accessibility in vivo include chromatin compaction and phase separation - processes that are beginning to be understood. Here, we review the ongoing development of accessibility measurements, we summarize the different molecular and structural mechanisms that shape the accessibility landscape, and we detail the many important biological functions that are linked to chromatin accessibility.

In situ tools for chromatin structural epigenomics

Technological progress over the past 15 years has fueled an explosion in genome-wide chromatin profiling tools that take advantage of low-cost short-read sequencing technologies to map particular chromatin features. Here, we survey the recent development of epigenomic tools that provide precise positions of chromatin proteins genome-wide in intact cells or nuclei. Some profiling tools are based on tethering Micrococcal Nuclease to chromatin proteins of interest in situ, whereas others similarly tether Tn5 transposase to integrate DNA sequencing adapters (tagmentation) and so eliminate the need for library preparation. These in situ cleavage and tagmentation tools have gained in popularity over the past few years, with many protocol enhancements and adaptations for single-cell and spatial chromatin profiling. The application of experimental and computational tools to address problems in gene regulation, eukaryotic development, and human disease are helping to define the emerging field of chromatin structural epigenomics.

sciCAN: single-cell chromatin accessibility and gene expression data integration via cycle-consistent adversarial network

The boom in single-cell technologies has brought a surge of high dimensional data that come from different sources and represent cellular systems from different views. With advances in these single-cell technologies, integrating single-cell data across modalities arises as a new computational challenge. Here, we present an adversarial approach, sciCAN, to integrate single-cell chromatin accessibility and gene expression data in an unsupervised manner. We benchmarked sciCAN with 5 existing methods in 5 scATAC-seq/scRNA-seq datasets, and we demonstrated that our method dealt with data integration with consistent performance across datasets and better balance of mutual transferring between modalities than the other 5 existing methods. We further applied sciCAN to 10X Multiome data and confirmed that the integrated representation preserves biological relationships within the hematopoietic hierarchy. Finally, we investigated CRISPR-perturbed single-cell K562 ATAC-seq and RNA-seq data to identify cells with related responses to different perturbations in these different modalities.

Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction

BACKGROUND

Current methods for analyzing single-cell datasets have relied primarily on static gene expression measurements to characterize the molecular state of individual cells. However, capturing temporal changes in cell state is crucial for the interpretation of dynamic phenotypes such as the cell cycle, development, or disease progression. RNA velocity infers the direction and speed of transcriptional changes in individual cells, yet it is unclear how these temporal gene expression modalities may be leveraged for predictive modeling of cellular dynamics.

RESULTS

Here, we present the first task-oriented benchmarking study that investigates integration of temporal sequencing modalities for dynamic cell state prediction. We benchmark ten integration approaches on ten datasets spanning different biological contexts, sequencing technologies, and species. We find that integrated data more accurately infers biological trajectories and achieves increased performance on classifying cells according to perturbation and disease states. Furthermore, we show that simple concatenation of spliced and unspliced molecules performs consistently well on classification tasks and can be used over more memory intensive and computationally expensive methods.

CONCLUSIONS

This work illustrates how integrated temporal gene expression modalities may be leveraged for predicting cellular trajectories and sample-associated perturbation and disease phenotypes. Additionally, this study provides users with practical recommendations for task-specific integration of single-cell gene expression modalities.

Into the multiverse: advances in single-cell multiomic profiling

Single-cell transcriptomic approaches have revolutionised the study of complex biological systems, with the routine measurement of gene expression in thousands of cells enabling construction of whole-organism cell atlases. However, the transcriptome is just one layer amongst many that coordinate to define cell type and state and, ultimately, function. In parallel with the widespread uptake of single-cell RNA-seq (scRNA-seq), there has been a rapid emergence of methods that enable multiomic profiling of individual cells, enabling parallel measurement of intercellular heterogeneity in the genome, epigenome, transcriptome, and proteomes. Linking measurements from each of these layers has the potential to reveal regulatory and functional mechanisms underlying cell behaviour in healthy development and disease.

Single cell cancer epigenetics

Bulk sequencing methodologies have allowed us to make great progress in cancer research. Unfortunately, these techniques lack the resolution to fully unravel the epigenetic mechanisms that govern tumor heterogeneity. Consequently, many novel single cell-sequencing methodologies have been developed over the past decade, allowing us to explore the epigenetic components that regulate different aspects of cancer heterogeneity, namely: clonal heterogeneity, tumor microenvironment (TME), spatial organization, intratumoral differentiation programs, metastasis, and resistance mechanisms. In this review, we explore the different sequencing techniques that enable researchers to study different aspects of epigenetics (DNA methylation, chromatin accessibility, histone modifications, DNA-protein interactions, and chromatin 3D architecture) at the single cell level, their potential applications in cancer, and their current technical limitations.

Breaking the Immune Complexity of the Tumor Microenvironment Using Single-Cell Technologies

Tumors are not a simple aggregate of transformed cells but rather a complicated ecosystem containing various components, including infiltrating immune cells, tumor-related stromal cells, endothelial cells, soluble factors, and extracellular matrix proteins. Profiling the immune contexture of this intricate framework is now mandatory to develop more effective cancer therapies and precise immunotherapeutic approaches by identifying exact targets or predictive biomarkers, respectively. Conventional technologies are limited in reaching this goal because they lack high resolution. Recent developments in single-cell technologies, such as single-cell RNA transcriptomics, mass cytometry, and multiparameter immunofluorescence, have revolutionized the cancer immunology field, capturing the heterogeneity of tumor-infiltrating immune cells and the dynamic complexity of tenets that regulate cell networks in the tumor microenvironment. In this review, we describe some of the current single-cell technologies and computational techniques applied for immune-profiling the cancer landscape and discuss future directions of how integrating multi-omics data can guide a new "precision oncology" advancement.

Surveying the Epigenetic Landscape of Tuberculosis in Alveolar Macrophages

Tuberculosis (TB) remains the leading cause of bacterial disease-related death and is among the top 10 overall causes of death worldwide. The complex nature of this infectious lung disease has proven difficult to treat, and significant research efforts are now evaluating the feasibility of host-directed, adjunctive therapies. An attractive approach in host-directed therapy targets host epigenetics, or gene regulation, to redirect the immune response in a host-beneficial manner. Substantial evidence exists demonstrating that host epigenetics are dysregulated during TB and that epigenetic-based therapies may be highly effective to treat TB. However, the caveat is that much of the knowledge that exists on the modulation of the host epigenome during TB has been gained using in vitro, small-animal, or blood-derived cell models, which do not accurately reflect the pulmonary nature of the disease. In humans, the first and major target cells of Mycobacterium tuberculosis are alveolar macrophages (AM). As such, their response to infection and treatment is clinically relevant and ultimately drives the outcome of disease. In this review, we compare the fundamental differences between AM and circulating monocyte-derived macrophages in the context of TB and summarize the recent advances in elucidating the epigenomes of these cells, including changes to the transcriptome, DNA methylome, and chromatin architecture. We will also discuss trained immunity in AM as a new and emerging field in TB research and provide some perspectives for the translational potential of targeting host epigenetics as an alternative TB therapy.